diff --git a/Pipfile b/Pipfile
index 9f41b6b6..ea3a2b0b 100644
--- a/Pipfile
+++ b/Pipfile
@@ -26,6 +26,9 @@ pandas = "==1.4.4"
 setuptools = "==63.4.1"
 pyparsing = "==3.0.9"
 jmespath = "==0.10.0"
+pydantic = "==2.0.0"
+rapidfuzz = ">=3.13.0"
+databricks-sdk = ">=0.62.0"
 
 [requires]
 python_version = "3.10.12"
diff --git a/Pipfile.lock b/Pipfile.lock
index d84b4997..ff11f0e7 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,11 +1,11 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "d8bd2400f4033b6b1de2a8ea04ec4e0fb9da66d94ee61f80542f21ed53e04681"
+            "sha256": "d74fa253aec9b1437ed5128071dfb05b41c6801cb9430a47ac0bc8fa9d2f059c"
         },
         "pipfile-spec": 6,
         "requires": {
-            "python_version": "3.8.12"
+            "python_version": "3.10.12"
         },
         "sources": [
             {
@@ -16,13 +16,147 @@
         ]
     },
     "default": {
+        "annotated-types": {
+            "hashes": [
+                "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53",
+                "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==0.7.0"
+        },
+        "cachetools": {
+            "hashes": [
+                "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4",
+                "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==5.5.2"
+        },
+        "certifi": {
+            "hashes": [
+                "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407",
+                "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==2025.8.3"
+        },
+        "charset-normalizer": {
+            "hashes": [
+                "sha256:00237675befef519d9af72169d8604a067d92755e84fe76492fef5441db05b91",
+                "sha256:02425242e96bcf29a49711b0ca9f37e451da7c70562bc10e8ed992a5a7a25cc0",
+                "sha256:027b776c26d38b7f15b26a5da1044f376455fb3766df8fc38563b4efbc515154",
+                "sha256:07a0eae9e2787b586e129fdcbe1af6997f8d0e5abaa0bc98c0e20e124d67e601",
+                "sha256:0cacf8f7297b0c4fcb74227692ca46b4a5852f8f4f24b3c766dd94a1075c4884",
+                "sha256:0e78314bdc32fa80696f72fa16dc61168fda4d6a0c014e0380f9d02f0e5d8a07",
+                "sha256:0f2be7e0cf7754b9a30eb01f4295cc3d4358a479843b31f328afd210e2c7598c",
+                "sha256:13faeacfe61784e2559e690fc53fa4c5ae97c6fcedb8eb6fb8d0a15b475d2c64",
+                "sha256:14c2a87c65b351109f6abfc424cab3927b3bdece6f706e4d12faaf3d52ee5efe",
+                "sha256:1606f4a55c0fd363d754049cdf400175ee96c992b1f8018b993941f221221c5f",
+                "sha256:16a8770207946ac75703458e2c743631c79c59c5890c80011d536248f8eaa432",
+                "sha256:18343b2d246dc6761a249ba1fb13f9ee9a2bcd95decc767319506056ea4ad4dc",
+                "sha256:18b97b8404387b96cdbd30ad660f6407799126d26a39ca65729162fd810a99aa",
+                "sha256:1bb60174149316da1c35fa5233681f7c0f9f514509b8e399ab70fea5f17e45c9",
+                "sha256:1e8ac75d72fa3775e0b7cb7e4629cec13b7514d928d15ef8ea06bca03ef01cae",
+                "sha256:1ef99f0456d3d46a50945c98de1774da86f8e992ab5c77865ea8b8195341fc19",
+                "sha256:2001a39612b241dae17b4687898843f254f8748b796a2e16f1051a17078d991d",
+                "sha256:23b6b24d74478dc833444cbd927c338349d6ae852ba53a0d02a2de1fce45b96e",
+                "sha256:252098c8c7a873e17dd696ed98bbe91dbacd571da4b87df3736768efa7a792e4",
+                "sha256:257f26fed7d7ff59921b78244f3cd93ed2af1800ff048c33f624c87475819dd7",
+                "sha256:2c322db9c8c89009a990ef07c3bcc9f011a3269bc06782f916cd3d9eed7c9312",
+                "sha256:30a96e1e1f865f78b030d65241c1ee850cdf422d869e9028e2fc1d5e4db73b92",
+                "sha256:30d006f98569de3459c2fc1f2acde170b7b2bd265dc1943e87e1a4efe1b67c31",
+                "sha256:31a9a6f775f9bcd865d88ee350f0ffb0e25936a7f930ca98995c05abf1faf21c",
+                "sha256:320e8e66157cc4e247d9ddca8e21f427efc7a04bbd0ac8a9faf56583fa543f9f",
+                "sha256:34a7f768e3f985abdb42841e20e17b330ad3aaf4bb7e7aeeb73db2e70f077b99",
+                "sha256:3653fad4fe3ed447a596ae8638b437f827234f01a8cd801842e43f3d0a6b281b",
+                "sha256:3cd35b7e8aedeb9e34c41385fda4f73ba609e561faedfae0a9e75e44ac558a15",
+                "sha256:3cfb2aad70f2c6debfbcb717f23b7eb55febc0bb23dcffc0f076009da10c6392",
+                "sha256:416175faf02e4b0810f1f38bcb54682878a4af94059a1cd63b8747244420801f",
+                "sha256:41d1fc408ff5fdfb910200ec0e74abc40387bccb3252f3f27c0676731df2b2c8",
+                "sha256:42e5088973e56e31e4fa58eb6bd709e42fc03799c11c42929592889a2e54c491",
+                "sha256:4ca4c094de7771a98d7fbd67d9e5dbf1eb73efa4f744a730437d8a3a5cf994f0",
+                "sha256:511729f456829ef86ac41ca78c63a5cb55240ed23b4b737faca0eb1abb1c41bc",
+                "sha256:53cd68b185d98dde4ad8990e56a58dea83a4162161b1ea9272e5c9182ce415e0",
+                "sha256:585f3b2a80fbd26b048a0be90c5aae8f06605d3c92615911c3a2b03a8a3b796f",
+                "sha256:5b413b0b1bfd94dbf4023ad6945889f374cd24e3f62de58d6bb102c4d9ae534a",
+                "sha256:5d8d01eac18c423815ed4f4a2ec3b439d654e55ee4ad610e153cf02faf67ea40",
+                "sha256:6aab0f181c486f973bc7262a97f5aca3ee7e1437011ef0c2ec04b5a11d16c927",
+                "sha256:6cf8fd4c04756b6b60146d98cd8a77d0cdae0e1ca20329da2ac85eed779b6849",
+                "sha256:6fb70de56f1859a3f71261cbe41005f56a7842cc348d3aeb26237560bfa5e0ce",
+                "sha256:6fce4b8500244f6fcb71465d4a4930d132ba9ab8e71a7859e6a5d59851068d14",
+                "sha256:70bfc5f2c318afece2f5838ea5e4c3febada0be750fcf4775641052bbba14d05",
+                "sha256:73dc19b562516fc9bcf6e5d6e596df0b4eb98d87e4f79f3ae71840e6ed21361c",
+                "sha256:74d77e25adda8581ffc1c720f1c81ca082921329452eba58b16233ab1842141c",
+                "sha256:78deba4d8f9590fe4dae384aeff04082510a709957e968753ff3c48399f6f92a",
+                "sha256:86df271bf921c2ee3818f0522e9a5b8092ca2ad8b065ece5d7d9d0e9f4849bcc",
+                "sha256:88ab34806dea0671532d3f82d82b85e8fc23d7b2dd12fa837978dad9bb392a34",
+                "sha256:8999f965f922ae054125286faf9f11bc6932184b93011d138925a1773830bbe9",
+                "sha256:8dcfc373f888e4fb39a7bc57e93e3b845e7f462dacc008d9749568b1c4ece096",
+                "sha256:939578d9d8fd4299220161fdd76e86c6a251987476f5243e8864a7844476ba14",
+                "sha256:96b2b3d1a83ad55310de8c7b4a2d04d9277d5591f40761274856635acc5fcb30",
+                "sha256:a2d08ac246bb48479170408d6c19f6385fa743e7157d716e144cad849b2dd94b",
+                "sha256:b256ee2e749283ef3ddcff51a675ff43798d92d746d1a6e4631bf8c707d22d0b",
+                "sha256:b5e3b2d152e74e100a9e9573837aba24aab611d39428ded46f4e4022ea7d1942",
+                "sha256:b89bc04de1d83006373429975f8ef9e7932534b8cc9ca582e4db7d20d91816db",
+                "sha256:bd28b817ea8c70215401f657edef3a8aa83c29d447fb0b622c35403780ba11d5",
+                "sha256:c60e092517a73c632ec38e290eba714e9627abe9d301c8c8a12ec32c314a2a4b",
+                "sha256:c6dbd0ccdda3a2ba7c2ecd9d77b37f3b5831687d8dc1b6ca5f56a4880cc7b7ce",
+                "sha256:c6e490913a46fa054e03699c70019ab869e990270597018cef1d8562132c2669",
+                "sha256:c6f162aabe9a91a309510d74eeb6507fab5fff92337a15acbe77753d88d9dcf0",
+                "sha256:c6fd51128a41297f5409deab284fecbe5305ebd7e5a1f959bee1c054622b7018",
+                "sha256:cc34f233c9e71701040d772aa7490318673aa7164a0efe3172b2981218c26d93",
+                "sha256:cc9370a2da1ac13f0153780040f465839e6cccb4a1e44810124b4e22483c93fe",
+                "sha256:ccf600859c183d70eb47e05a44cd80a4ce77394d1ac0f79dbd2dd90a69a3a049",
+                "sha256:ce571ab16d890d23b5c278547ba694193a45011ff86a9162a71307ed9f86759a",
+                "sha256:cf1ebb7d78e1ad8ec2a8c4732c7be2e736f6e5123a4146c5b89c9d1f585f8cef",
+                "sha256:d0e909868420b7049dafd3a31d45125b31143eec59235311fc4c57ea26a4acd2",
+                "sha256:d22dbedd33326a4a5190dd4fe9e9e693ef12160c77382d9e87919bce54f3d4ca",
+                "sha256:d716a916938e03231e86e43782ca7878fb602a125a91e7acb8b5112e2e96ac16",
+                "sha256:d79c198e27580c8e958906f803e63cddb77653731be08851c7df0b1a14a8fc0f",
+                "sha256:d95bfb53c211b57198bb91c46dd5a2d8018b3af446583aab40074bf7988401cb",
+                "sha256:e28e334d3ff134e88989d90ba04b47d84382a828c061d0d1027b1b12a62b39b1",
+                "sha256:ec557499516fc90fd374bf2e32349a2887a876fbf162c160e3c01b6849eaf557",
+                "sha256:fb6fecfd65564f208cbf0fba07f107fb661bcd1a7c389edbced3f7a493f70e37",
+                "sha256:fb731e5deb0c7ef82d698b0f4c5bb724633ee2a489401594c5c88b02e6cb15f7",
+                "sha256:fb7f67a1bfa6e40b438170ebdc8158b78dc465a5a67b6dde178a46987b244a72",
+                "sha256:fd10de089bcdcd1be95a2f73dbe6254798ec1bda9f450d5828c96f93e2536b9c",
+                "sha256:fdabf8315679312cfa71302f9bd509ded4f2f263fb5b765cf1433b39106c3cc9"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==3.4.3"
+        },
+        "databricks-sdk": {
+            "hashes": [
+                "sha256:12f8da735f74cba5265dcb0620c30941628f63787e6ea7e57cc5ed22d9e1b987",
+                "sha256:79d4abe60306239985a5b718583923e13316c5417204017b0dadf74bccfcc6e1"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.7'",
+            "version": "==0.62.0"
+        },
+        "google-auth": {
+            "hashes": [
+                "sha256:1370d4593e86213563547f97a92752fc658456fe4514c809544f330fed45a7ca",
+                "sha256:500c3a29adedeb36ea9cf24b8d10858e152f2412e3ca37829b3fa18e33d63b77"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==2.40.3"
+        },
+        "idna": {
+            "hashes": [
+                "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9",
+                "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==3.10"
+        },
         "jmespath": {
             "hashes": [
                 "sha256:b85d0567b8666149a93172712e68920734333c0ce7e89b78b3e987f71e5ed4f9",
                 "sha256:cdf6525904cc597730141d61b36f2e4b8ecc257c420fa2f4549bac2c2d0cb72f"
             ],
             "index": "pypi",
-            "markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2'",
             "version": "==0.10.0"
         },
         "numpy": {
@@ -56,123 +190,378 @@
         },
         "pandas": {
             "hashes": [
-                "sha256:167693a80abc8eb28051fbd184c1b7afd13ce2c727a5af47b048f1ea3afefff4",
-                "sha256:2111c25e69fa9365ba80bbf4f959400054b2771ac5d041ed19415a8b488dc70a",
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-                "sha256:612add929bf3ba9d27b436cc8853f5acc337242d6b584203f207e364bb46cb12",
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-                "sha256:c601c6fdebc729df4438ec1f62275d6136a0dd14d332fc0e8ce3f7d2aadb4dd6",
-                "sha256:d0877407359811f7b853b548a614aacd7dea83b0c0c84620a9a643f180060950"
+                "sha256:050aada67a5ec6699a7879e769825b510018a95fb9ac462bb1867483d0974a97",
+                "sha256:0959c41004e3d2d16f39c828d6da66ebee329836a7ecee49fb777ac9ad8a7501",
+                "sha256:4591cadd06fbbbd16fafc2de6e840c1aaefeae3d5864b688004777ef1bbdede3",
+                "sha256:51c424ca134fdaeac9a4acd719d1ab48046afc60943a489028f0413fdbe9ef1c",
+                "sha256:785e878a6e6d8ddcdb8c181e600855402750052497d7fc6d6b508894f6b8830b",
+                "sha256:799e6a25932df7e6b1f8dabf63de064e2205dc309abb75956126a0453fd88e97",
+                "sha256:7cd1d69a387f7d5e1a5a06a87574d9ef2433847c0e78113ab51c84d3a8bcaeaa",
+                "sha256:87b4194f344dcd14c0f885cecb22005329b38bda10f1aaf7b9596a00ec8a4768",
+                "sha256:8d4d2fe2863ecddb0ba1979bdda26c8bc2ea138f5a979abe3ba80c0fa4015c91",
+                "sha256:94f2ed1fd51e545ebf71da1e942fe1822ee01e10d3dd2a7276d01351333b7c6b",
+                "sha256:9d2a7a3c1fea668d56bd91edbd5f2732e0af8feb9d2bf8d9bfacb2dea5fa9536",
+                "sha256:9d805bce209714b1c1fa29bfb1e42ad87e4c0a825e4b390c56a3e71593b7e8d8",
+                "sha256:a08ceb59db499864c58a9bf85ab6219d527d91f14c0240cc25fa2c261032b2a7",
+                "sha256:a981cfabf51c318a562deb4ae7deec594c07aee7cf18b4594a92c23718ec8275",
+                "sha256:ab6c0d738617b675183e5f28db32b5148b694ad9bba0a40c3ea26d96b431db67",
+                "sha256:afbddad78a98ec4d2ce08b384b81730de1ccc975b99eb663e6dac43703f36d98",
+                "sha256:c4bb8b0ab9f94207d07e401d24baebfc63057246b1a5e0cd9ee50df85a656871",
+                "sha256:ce35f947202b0b99c660221d82beb91d2e6d553d55a40b30128204e3e2c63848",
+                "sha256:d0022fe6a313df1c4869b5edc012d734c6519a6fffa3cf70930f32e6a1078e49",
+                "sha256:e7cc960959be28d064faefc0cb2aef854d46b827c004ebea7e79b5497ed83e7d",
+                "sha256:ee6f1848148ed3204235967613b0a32be2d77f214e9623f554511047705c1e04"
             ],
             "index": "pypi",
-            "markers": "python_full_version >= '3.7.1'",
-            "version": "==1.2.4"
+            "markers": "python_version >= '3.8'",
+            "version": "==1.4.4"
         },
         "py4j": {
             "hashes": [
-                "sha256:36ec57f43ff8ced260a18aa9a4e46c3500a730cac8860e259cbaa546c2b9db2f",
-                "sha256:859ba728a7bb43e9c2bf058832759fb97a598bb28cc12f34f5fc4abdec08ede6"
+                "sha256:0b6e5315bb3ada5cf62ac651d107bb2ebc02def3dee9d9548e3baac644ea8dbb",
+                "sha256:85defdfd2b2376eb3abf5ca6474b51ab7e0de341c75a02f46dc9b5976f5a5c1b"
             ],
-            "version": "==0.10.9"
+            "version": "==0.10.9.7"
         },
         "pyarrow": {
             "hashes": [
-                "sha256:04be0f7cb9090bd029b5b53bed628548fef569e5d0b5c6cd7f6d0106dbbc782d",
-                "sha256:0fde9c7a3d5d37f3fe5d18c4ed015e8f585b68b26d72a10d7012cad61afe43ff",
-                "sha256:11517f0b4f4acbab0c37c674b4d1aad3c3dfea0f6b1bb322e921555258101ab3",
-                "sha256:150db335143edd00d3ec669c7c8167d401c4aa0a290749351c80bbf146892b2e",
-                "sha256:24040a20208e9b16ba7b284624ebfe67e40f5c40b5dc8d874da322ac0053f9d3",
-                "sha256:33c457728a1ce825b80aa8c8ed573709f1efe72003d45fa6fdbb444de9cc0b74",
-                "sha256:423cd6a14810f4e40cb76e13d4240040fc1594d69fe1c4f2c70be00ad512ade5",
-                "sha256:5387db80c6a7b5598884bf4df3fc546b3373771ad614548b782e840b71704877",
-                "sha256:5a76ec44af838862b23fb5cfc48765bc7978f7b58a181c96ad92856280de548b",
-                "sha256:5f2660f59dfcfd34adac7c08dc7f615920de703f191066ed6277628975f06878",
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@@ -189,32 +578,40 @@
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@@ -225,19 +622,22 @@
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@@ -257,107 +657,96 @@
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         },
         "commonmark": {
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@@ -371,69 +760,105 @@
                 "toml"
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@@ -445,19 +870,19 @@
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@@ -468,26 +893,34 @@
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@@ -498,27 +931,27 @@
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@@ -530,84 +963,84 @@
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@@ -744,27 +1135,27 @@
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@@ -776,12 +1167,12 @@
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@@ -792,11 +1183,11 @@
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@@ -838,36 +1229,36 @@
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@@ -876,13 +1267,20 @@
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@@ -903,27 +1301,27 @@
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@@ -933,210 +1331,211 @@
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@@ -1350,27 +1806,27 @@
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@@ -1406,52 +1869,83 @@
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+                "sha256:6972ca9c9cc9f0acaa56a8ca1ff51e7af152a9f87fb64623e31d5c83700080ee",
+                "sha256:7fc04e92e1d624a4a63c76474610238576942d6b8950a2d7f908a340494e67e4",
+                "sha256:889f80ef92701b9dbb224e49ec87c645ce5df3fa2cc548664eb8a25e03127a98",
+                "sha256:8d57ca8095a641b8237d5b079147646153d22552f1c637fd3ba7f4b0b29167a8",
+                "sha256:8dd28b3e155b80f4d54beb40a441d366adcfe740969820caf156c019fb5c7ec4",
+                "sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281",
+                "sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744",
+                "sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69",
+                "sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13",
+                "sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140",
+                "sha256:b82ebccc8c8a36f2094e969560a1b836758481f3dc360ce9a3277c65f374285e",
+                "sha256:c954d2250168d28797dd4e3ac5cf812a406cd5a92674ee4c8f123c889786aa8e",
+                "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc",
+                "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff",
+                "sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec",
+                "sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2",
+                "sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222",
+                "sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106",
+                "sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272",
+                "sha256:ece47d672db52ac607a3d9599a9d48dcb2f2f735c6c2d1f34130085bb12b112a",
+                "sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7"
             ],
-            "markers": "python_version < '3.11'",
-            "version": "==2.0.1"
+            "markers": "python_version >= '3.8'",
+            "version": "==2.2.1"
         },
         "tomlkit": {
             "hashes": [
-                "sha256:af914f5a9c59ed9d0762c7b64d3b5d5df007448eb9cd2edc8a46b1eafead172f",
-                "sha256:eef34fba39834d4d6b73c9ba7f3e4d1c417a4e56f89a7e96e090dd0d24b8fb3c"
+                "sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1",
+                "sha256:c89c649d79ee40629a9fda55f8ace8c6a1b42deb912b2a8fd8d942ddadb606b0"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==0.12.5"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.13.3"
         },
         "tornado": {
             "hashes": [
-                "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0",
-                "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63",
-                "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263",
-                "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052",
-                "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f",
-                "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee",
-                "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78",
-                "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579",
-                "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212",
-                "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e",
-                "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"
+                "sha256:06ceb1300fd70cb20e43b1ad8aaee0266e69e7ced38fa910ad2e03285009ce7c",
+                "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6",
+                "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef",
+                "sha256:74db443e0f5251be86cbf37929f84d8c20c27a355dd452a5cfa2aada0d001ec4",
+                "sha256:ab53c8f9a0fa351e2c0741284e06c7a45da86afb544133201c5cc8578eb076a0",
+                "sha256:b0fe179f28d597deab2842b86ed4060deec7388f1fd9c1b4a41adf8af058907e",
+                "sha256:b186e85d1e3536d69583d2298423744740986018e393d0321df7340e71898882",
+                "sha256:b5e735ab2889d7ed33b32a459cac490eda71a1ba6857b0118de476ab6c366c04",
+                "sha256:c6f29e94d9b37a95013bb669616352ddb82e3bfe8326fccee50583caebc8a5f0",
+                "sha256:d6c33dc3672e3a1f3618eb63b7ef4683a7688e7b9e6e8f0d9aa5726360a004af",
+                "sha256:e56a5af51cc30dd2cae649429af65ca2f6571da29504a07995175df14c18f35f",
+                "sha256:e792706668c87709709c18b353da1f7662317b563ff69f00bab83595940c7108"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==6.4"
+            "markers": "python_version >= '3.9'",
+            "version": "==6.5.2"
         },
         "traitlets": {
             "hashes": [
@@ -1463,19 +1957,19 @@
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a",
-                "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"
+                "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36",
+                "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"
             ],
-            "markers": "python_version < '3.10'",
-            "version": "==4.12.1"
+            "markers": "python_version >= '3.9'",
+            "version": "==4.14.1"
         },
         "urllib3": {
             "hashes": [
-                "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d",
-                "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"
+                "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760",
+                "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==2.2.1"
+            "markers": "python_version >= '3.9'",
+            "version": "==2.5.0"
         },
         "wcwidth": {
             "hashes": [
@@ -1491,89 +1985,13 @@
             ],
             "version": "==0.5.1"
         },
-        "wrapt": {
-            "hashes": [
-                "sha256:0d2691979e93d06a95a26257adb7bfd0c93818e89b1406f5a28f36e0d8c1e1fc",
-                "sha256:14d7dc606219cdd7405133c713f2c218d4252f2a469003f8c46bb92d5d095d81",
-                "sha256:1a5db485fe2de4403f13fafdc231b0dbae5eca4359232d2efc79025527375b09",
-                "sha256:1acd723ee2a8826f3d53910255643e33673e1d11db84ce5880675954183ec47e",
-                "sha256:1ca9b6085e4f866bd584fb135a041bfc32cab916e69f714a7d1d397f8c4891ca",
-                "sha256:1dd50a2696ff89f57bd8847647a1c363b687d3d796dc30d4dd4a9d1689a706f0",
-                "sha256:2076fad65c6736184e77d7d4729b63a6d1ae0b70da4868adeec40989858eb3fb",
-                "sha256:2a88e6010048489cda82b1326889ec075a8c856c2e6a256072b28eaee3ccf487",
-                "sha256:3ebf019be5c09d400cf7b024aa52b1f3aeebeff51550d007e92c3c1c4afc2a40",
-                "sha256:418abb18146475c310d7a6dc71143d6f7adec5b004ac9ce08dc7a34e2babdc5c",
-                "sha256:43aa59eadec7890d9958748db829df269f0368521ba6dc68cc172d5d03ed8060",
-                "sha256:44a2754372e32ab315734c6c73b24351d06e77ffff6ae27d2ecf14cf3d229202",
-                "sha256:490b0ee15c1a55be9c1bd8609b8cecd60e325f0575fc98f50058eae366e01f41",
-                "sha256:49aac49dc4782cb04f58986e81ea0b4768e4ff197b57324dcbd7699c5dfb40b9",
-                "sha256:5eb404d89131ec9b4f748fa5cfb5346802e5ee8836f57d516576e61f304f3b7b",
-                "sha256:5f15814a33e42b04e3de432e573aa557f9f0f56458745c2074952f564c50e664",
-                "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d",
-                "sha256:66027d667efe95cc4fa945af59f92c5a02c6f5bb6012bff9e60542c74c75c362",
-                "sha256:66dfbaa7cfa3eb707bbfcd46dab2bc6207b005cbc9caa2199bcbc81d95071a00",
-                "sha256:685f568fa5e627e93f3b52fda002c7ed2fa1800b50ce51f6ed1d572d8ab3e7fc",
-                "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1",
-                "sha256:6a42cd0cfa8ffc1915aef79cb4284f6383d8a3e9dcca70c445dcfdd639d51267",
-                "sha256:6dcfcffe73710be01d90cae08c3e548d90932d37b39ef83969ae135d36ef3956",
-                "sha256:6f6eac2360f2d543cc875a0e5efd413b6cbd483cb3ad7ebf888884a6e0d2e966",
-                "sha256:72554a23c78a8e7aa02abbd699d129eead8b147a23c56e08d08dfc29cfdddca1",
-                "sha256:73870c364c11f03ed072dda68ff7aea6d2a3a5c3fe250d917a429c7432e15228",
-                "sha256:73aa7d98215d39b8455f103de64391cb79dfcad601701a3aa0dddacf74911d72",
-                "sha256:75ea7d0ee2a15733684badb16de6794894ed9c55aa5e9903260922f0482e687d",
-                "sha256:7bd2d7ff69a2cac767fbf7a2b206add2e9a210e57947dd7ce03e25d03d2de292",
-                "sha256:807cc8543a477ab7422f1120a217054f958a66ef7314f76dd9e77d3f02cdccd0",
-                "sha256:8e9723528b9f787dc59168369e42ae1c3b0d3fadb2f1a71de14531d321ee05b0",
-                "sha256:9090c9e676d5236a6948330e83cb89969f433b1943a558968f659ead07cb3b36",
-                "sha256:9153ed35fc5e4fa3b2fe97bddaa7cbec0ed22412b85bcdaf54aeba92ea37428c",
-                "sha256:9159485323798c8dc530a224bd3ffcf76659319ccc7bbd52e01e73bd0241a0c5",
-                "sha256:941988b89b4fd6b41c3f0bfb20e92bd23746579736b7343283297c4c8cbae68f",
-                "sha256:94265b00870aa407bd0cbcfd536f17ecde43b94fb8d228560a1e9d3041462d73",
-                "sha256:98b5e1f498a8ca1858a1cdbffb023bfd954da4e3fa2c0cb5853d40014557248b",
-                "sha256:9b201ae332c3637a42f02d1045e1d0cccfdc41f1f2f801dafbaa7e9b4797bfc2",
-                "sha256:a0ea261ce52b5952bf669684a251a66df239ec6d441ccb59ec7afa882265d593",
-                "sha256:a33a747400b94b6d6b8a165e4480264a64a78c8a4c734b62136062e9a248dd39",
-                "sha256:a452f9ca3e3267cd4d0fcf2edd0d035b1934ac2bd7e0e57ac91ad6b95c0c6389",
-                "sha256:a86373cf37cd7764f2201b76496aba58a52e76dedfaa698ef9e9688bfd9e41cf",
-                "sha256:ac83a914ebaf589b69f7d0a1277602ff494e21f4c2f743313414378f8f50a4cf",
-                "sha256:aefbc4cb0a54f91af643660a0a150ce2c090d3652cf4052a5397fb2de549cd89",
-                "sha256:b3646eefa23daeba62643a58aac816945cadc0afaf21800a1421eeba5f6cfb9c",
-                "sha256:b47cfad9e9bbbed2339081f4e346c93ecd7ab504299403320bf85f7f85c7d46c",
-                "sha256:b935ae30c6e7400022b50f8d359c03ed233d45b725cfdd299462f41ee5ffba6f",
-                "sha256:bb2dee3874a500de01c93d5c71415fcaef1d858370d405824783e7a8ef5db440",
-                "sha256:bc57efac2da352a51cc4658878a68d2b1b67dbe9d33c36cb826ca449d80a8465",
-                "sha256:bf5703fdeb350e36885f2875d853ce13172ae281c56e509f4e6eca049bdfb136",
-                "sha256:c31f72b1b6624c9d863fc095da460802f43a7c6868c5dda140f51da24fd47d7b",
-                "sha256:c5cd603b575ebceca7da5a3a251e69561bec509e0b46e4993e1cac402b7247b8",
-                "sha256:d2efee35b4b0a347e0d99d28e884dfd82797852d62fcd7ebdeee26f3ceb72cf3",
-                "sha256:d462f28826f4657968ae51d2181a074dfe03c200d6131690b7d65d55b0f360f8",
-                "sha256:d5e49454f19ef621089e204f862388d29e6e8d8b162efce05208913dde5b9ad6",
-                "sha256:da4813f751142436b075ed7aa012a8778aa43a99f7b36afe9b742d3ed8bdc95e",
-                "sha256:db2e408d983b0e61e238cf579c09ef7020560441906ca990fe8412153e3b291f",
-                "sha256:db98ad84a55eb09b3c32a96c576476777e87c520a34e2519d3e59c44710c002c",
-                "sha256:dbed418ba5c3dce92619656802cc5355cb679e58d0d89b50f116e4a9d5a9603e",
-                "sha256:dcdba5c86e368442528f7060039eda390cc4091bfd1dca41e8046af7c910dda8",
-                "sha256:decbfa2f618fa8ed81c95ee18a387ff973143c656ef800c9f24fb7e9c16054e2",
-                "sha256:e4fdb9275308292e880dcbeb12546df7f3e0f96c6b41197e0cf37d2826359020",
-                "sha256:eb1b046be06b0fce7249f1d025cd359b4b80fc1c3e24ad9eca33e0dcdb2e4a35",
-                "sha256:eb6e651000a19c96f452c85132811d25e9264d836951022d6e81df2fff38337d",
-                "sha256:ed867c42c268f876097248e05b6117a65bcd1e63b779e916fe2e33cd6fd0d3c3",
-                "sha256:edfad1d29c73f9b863ebe7082ae9321374ccb10879eeabc84ba3b69f2579d537",
-                "sha256:f2058f813d4f2b5e3a9eb2eb3faf8f1d99b81c3e51aeda4b168406443e8ba809",
-                "sha256:f6b2d0c6703c988d334f297aa5df18c45e97b0af3679bb75059e0e0bd8b1069d",
-                "sha256:f8212564d49c50eb4565e502814f694e240c55551a5f1bc841d4fcaabb0a9b8a",
-                "sha256:ffa565331890b90056c01db69c0fe634a776f8019c143a5ae265f9c6bc4bd6d4"
-            ],
-            "markers": "python_version < '3.11'",
-            "version": "==1.16.0"
-        },
         "zipp": {
             "hashes": [
-                "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19",
-                "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"
+                "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e",
+                "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"
             ],
-            "markers": "python_version >= '3.8'",
-            "version": "==3.19.2"
+            "markers": "python_version >= '3.9'",
+            "version": "==3.23.0"
         }
     }
 }
diff --git a/dbldatagen/extensions/__init__.py b/dbldatagen/extensions/__init__.py
new file mode 100644
index 00000000..a383dd27
--- /dev/null
+++ b/dbldatagen/extensions/__init__.py
@@ -0,0 +1,16 @@
+"""
+Extension modules for dbldatagen.
+
+This package contains optional extensions that can be installed via:
+    pip install dbldatagen[extension]
+"""
+
+__version__ = "0.1.0" 
+
+
+try:
+    from .spec_generators.uc_generator import DatabricksUCSpecGenerator
+    from .spec_generators.employee_generator import EmployeeSpecGenerator
+    __all__ = ['DatabricksUCSpecGenerator', 'EmployeeSpecGenerator']
+except ImportError:
+    __all__ = []
\ No newline at end of file
diff --git a/dbldatagen/extensions/data_generator_from_spec.py b/dbldatagen/extensions/data_generator_from_spec.py
new file mode 100644
index 00000000..d477cc20
--- /dev/null
+++ b/dbldatagen/extensions/data_generator_from_spec.py
@@ -0,0 +1,252 @@
+import logging
+from typing import Dict, Union
+import posixpath
+from pyspark.sql import SparkSession
+import dbldatagen as dg
+from .datagen_spec import DatagenSpec, UCSchemaTarget, FilePathTarget, ColumnDefinition
+
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+logger = logging.getLogger(__name__)
+
+INTERNAL_ID_COLUMN_NAME = "id"
+
+
+class Generator:
+    """
+    Main data generation orchestrator that handles configuration, preparation, and writing of data.
+    """
+
+    def __init__(self, spark: SparkSession, app_name: str = "DataGen_ClassBased"):
+        """
+        Initialize the Generator with a SparkSession.
+        Args:
+            spark: An existing SparkSession instance
+            app_name: Application name for logging purposes
+        Raises:
+            RuntimeError: If spark is None
+        """
+        if not spark:
+            logger.error(
+                "SparkSession cannot be None during Generator initialization")
+            raise RuntimeError("SparkSession cannot be None")
+        self.spark = spark
+        self._created_spark_session = False
+        self.app_name = app_name
+        logger.info("Generator initialized with SparkSession")
+
+    def _columnspec_to_datagen_columnspec(self, col_def: ColumnDefinition) -> Dict[str, str]:
+        """
+        Convert a ColumnDefinition to dbldatagen column specification.
+        Args:
+            col_def: ColumnDefinition object containing column configuration
+        Returns:
+            Dictionary containing dbldatagen column specification
+        """
+        col_name = col_def.name
+        col_type = col_def.type
+        kwargs = col_def.options.copy() if col_def.options is not None else {}
+
+        if col_def.primary:
+            kwargs["colType"] = col_type
+            kwargs["baseColumn"] = INTERNAL_ID_COLUMN_NAME
+
+            if col_type == "string":
+                kwargs["baseColumnType"] = "hash"
+            elif col_type not in ["int", "long", "integer", "bigint", "short"]:
+                kwargs["baseColumnType"] = "auto"
+                logger.warning(
+                    f"Primary key '{col_name}' has non-standard type '{col_type}'")
+
+            # Log conflicting options for primary keys
+            conflicting_opts_for_pk = [
+                "distribution", "template", "dataRange", "random", "omit",
+                "min", "max", "uniqueValues", "values", "expr"
+            ]
+
+            for opt_key in conflicting_opts_for_pk:
+                if opt_key in kwargs:
+                    logger.warning(
+                        f"Primary key '{col_name}': Option '{opt_key}' may be ignored")
+
+            if col_def.omit is not None and col_def.omit:
+                kwargs["omit"] = True
+        else:
+            kwargs = col_def.options.copy() if col_def.options is not None else {}
+
+            if col_type:
+                kwargs["colType"] = col_type
+            if col_def.baseColumn:
+                kwargs["baseColumn"] = col_def.baseColumn
+            if col_def.baseColumnType:
+                kwargs["baseColumnType"] = col_def.baseColumnType
+            if col_def.omit is not None:
+                kwargs["omit"] = col_def.omit
+
+        return kwargs
+
+    def prepare_data_generators(
+        self,
+        config: DatagenSpec,
+        config_source_name: str = "PydanticConfig"
+    ) -> Dict[str, dg.DataGenerator]:
+        """
+        Prepare DataGenerator specifications for each table based on the configuration.
+        Args:
+            config: DatagenSpec Pydantic object containing table configurations
+            config_source_name: Name for the configuration source (for logging)
+        Returns:
+            Dictionary mapping table names to their configured dbldatagen.DataGenerator objects
+        Raises:
+            RuntimeError: If SparkSession is not available
+            ValueError: If any table preparation fails
+            Exception: If any unexpected error occurs during preparation
+        """
+        logger.info(
+            f"Preparing data generators for {len(config.tables)} tables")
+
+        if not self.spark:
+            logger.error(
+                "SparkSession is not available. Cannot prepare data generators")
+            raise RuntimeError(
+                "SparkSession is not available. Cannot prepare data generators")
+
+        tables_config = config.tables
+        global_gen_options = config.generator_options if config.generator_options else {}
+
+        prepared_generators: Dict[str, dg.DataGenerator] = {}
+        generation_order = list(tables_config.keys())
+
+        for table_name in generation_order:
+            table_spec = tables_config[table_name]
+            logger.info(f"Preparing table: {table_name}")
+
+            try:
+                # Create DataGenerator instance
+                data_gen = dg.DataGenerator(
+                    sparkSession=self.spark,
+                    name=f"{table_name}_spec_from_{config_source_name}",
+                    rows=table_spec.number_of_rows,
+                    partitions=table_spec.partitions,
+                    **global_gen_options,
+                )
+
+                # Process each column
+                for col_def in table_spec.columns:
+                    kwargs = self._columnspec_to_datagen_columnspec(col_def)
+                    data_gen = data_gen.withColumn(
+                        colName=col_def.name, **kwargs)
+
+                prepared_generators[table_name] = data_gen
+                logger.info(f"Successfully prepared table: {table_name}")
+
+            except Exception as e:
+                logger.error(f"Failed to prepare table '{table_name}': {e}")
+                raise RuntimeError(
+                    f"Failed to prepare table '{table_name}': {e}") from e
+
+        logger.info("All data generators prepared successfully")
+        return prepared_generators
+
+    def write_prepared_data(
+        self,
+        prepared_generators: Dict[str, dg.DataGenerator],
+        output_destination: Union[UCSchemaTarget, FilePathTarget, None],
+        config_source_name: str = "PydanticConfig",
+    ) -> None:
+        """
+        Write data from prepared generators to the specified output destination.
+
+        Args:
+            prepared_generators: Dictionary of prepared DataGenerator objects
+            output_destination: Target destination for data output
+            config_source_name: Name for the configuration source (for logging)
+
+        Raises:
+            RuntimeError: If any table write fails
+            ValueError: If output destination is not properly configured
+        """
+        logger.info("Starting data writing phase")
+
+        if not prepared_generators:
+            logger.warning("No prepared data generators to write")
+            return
+
+        for table_name, data_gen in prepared_generators.items():
+            logger.info(f"Writing table: {table_name}")
+
+            try:
+                df = data_gen.build()
+                requested_rows = data_gen.rowCount
+                actual_row_count = df.count()
+                logger.info(
+                    f"Built DataFrame for '{table_name}':{actual_row_count} rows (requested: {requested_rows})")
+
+                if actual_row_count == 0 and requested_rows > 0:
+                    logger.warning(f"Table '{table_name}': Requested {requested_rows} rows but built 0")
+
+                # Write data based on destination type
+                if isinstance(output_destination, FilePathTarget):
+                    output_path = posixpath.join(output_destination.base_path, table_name)
+                    df.write.format(output_destination.output_format).save(output_path)
+                    logger.info(f"Wrote table '{table_name}' to file path")
+
+                elif isinstance(output_destination, UCSchemaTarget):
+                    output_table = f"{output_destination.catalog}.{output_destination.schema_}.{table_name}"
+                    df.write.mode("overwrite").saveAsTable(output_table)
+                    logger.info(f"Wrote table '{table_name}' to Unity Catalog")
+                else:
+                    logger.warning("No output destination specified, skipping data write")
+                    return
+            except Exception as e:
+                logger.error(f"Failed to write table '{table_name}': {e}")
+                raise RuntimeError(f"Failed to write table '{table_name}': {e}") from e
+        logger.info("All data writes completed successfully")
+
+    def generate_and_write_data(
+        self,
+        config: DatagenSpec,
+        config_source_name: str = "PydanticConfig"
+    ) -> None:
+        """
+        Combined method to prepare data generators and write data in one operation.
+        This method orchestrates the complete data generation workflow:
+        1. Prepare data generators from configuration
+        2. Write data to the specified destination
+        Args:
+            config: DatagenSpec Pydantic object containing table configurations
+            config_source_name: Name for the configuration source (for logging)
+        Raises:
+            RuntimeError: If SparkSession is not available or any step fails
+            ValueError: If critical errors occur during preparation or writing
+        """
+        logger.info(f"Starting combined data generation and writing for {len(config.tables)} tables")
+
+        try:
+            # Phase 1: Prepare data generators
+            prepared_generators_map = self.prepare_data_generators(config, config_source_name)
+
+            if not prepared_generators_map and list(config.tables.keys()):
+                logger.warning(
+                    "No data generators were successfully prepared, though tables were defined")
+                return
+
+            # Phase 2: Write data
+            self.write_prepared_data(
+                prepared_generators_map,
+                config.output_destination,
+                config_source_name
+            )
+
+            logger.info(
+                "Combined data generation and writing completed successfully")
+
+        except Exception as e:
+            logger.error(
+                f"Error during combined data generation and writing: {e}")
+            raise RuntimeError(
+                f"Error during combined data generation and writing: {e}") from e
diff --git a/dbldatagen/extensions/datagen_spec.py b/dbldatagen/extensions/datagen_spec.py
new file mode 100644
index 00000000..14a9184a
--- /dev/null
+++ b/dbldatagen/extensions/datagen_spec.py
@@ -0,0 +1,190 @@
+import os
+import logging
+from enum import Enum
+from typing import List, Dict, Optional, Any, Literal, Union
+from pydantic import BaseModel, model_validator, field_validator
+import pandas as pd
+from IPython.display import display, HTML
+
+
+logger = logging.getLogger(__name__)
+
+
+class PathType(Enum):
+    UC_TABLE = "Unity Catalog Table"
+    UC_VOLUME = "Unity Catalog Volume"
+    RELATIVE = "Relative Path"
+    ABSOLUTE_NON_UC = "Absolute Non-UC Path"
+    UNKNOWN = "Unknown or Invalid Path Structure"
+
+
+# Allowed (PathType, output_format_lowercase) combinations
+_OFF_DB_STRICT_ALLOWED_COMBINATIONS = {
+    (PathType.RELATIVE, "delta"),
+    (PathType.RELATIVE, "parquet"),
+    (PathType.RELATIVE, "csv"),
+    # Add more formats like orc, json, text as needed
+}
+
+_DATABRICKS_COMPATIBLE_ALLOWED_COMBINATIONS = {
+    (PathType.UC_TABLE, "delta"),
+    (PathType.UC_VOLUME, "parquet"),
+    (PathType.UC_VOLUME, "csv"),
+    # Add more formats like orc, json, text as needed
+}
+
+
+DbldatagenBasicType = Literal[
+    "string",
+    "int",
+    "long",
+    "float",
+    "double",
+    "decimal",
+    "boolean",
+    "date",
+    "timestamp",
+    "short",
+    "byte",
+    "binary",
+    "integer",
+    "bigint",
+    "tinyint",
+]
+
+
+# Note: There is no way to specify the per table output.
+# One will have to create a different config for each schema
+class UCSchemaTarget(BaseModel):
+    catalog: str
+    schema_: str
+    output_format: str = "delta"  # Default to delta for UC Schema
+
+    @field_validator("catalog", "schema_", mode="after")
+    def validate_identifiers(cls, v):  # noqa: N805, pylint: disable=no-self-argument
+        if not v.strip():
+            raise ValueError("Identifier must be non-empty.")
+        if not v.isidentifier():
+            logger.warning(
+                f"'{v}' is not a basic Python identifier. Ensure validity for Unity Catalog.")
+        return v.strip()
+
+    def __str__(self):
+        return f"{self.catalog}.{self.schema_} (Format: {self.output_format}, Type: UC Table)"
+
+
+class FilePathTarget(BaseModel):
+    base_path: str
+    output_format: Literal["csv", "parquet"]  # No default, must be specified
+
+    @field_validator("base_path", mode="after")
+    def validate_base_path(cls, v):  # noqa: N805, pylint: disable=no-self-argument
+        if not v.strip():
+            raise ValueError("base_path must be non-empty.")
+        return v.strip()
+
+    def __str__(self):
+        return f"{self.base_path} (Format: {self.output_format}, Type: File Path)"
+
+
+class ColumnDefinition(BaseModel):
+    name: str
+    type: Optional[DbldatagenBasicType] = None
+    primary: bool = False
+    options: Optional[Dict[str, Any]] = {}
+    nullable: Optional[bool] = False
+    omit: Optional[bool] = False
+    baseColumn: Optional[str] = "id"
+    baseColumnType: Optional[str] = "auto"
+
+    @model_validator(mode="after")
+    def check_constraints(self):
+        if self.primary:
+            if "min" in self.options or "max" in self.options:
+                raise ValueError(
+                    f"Primary column '{self.name}' cannot have min/max options.")
+            if self.nullable:
+                raise ValueError(
+                    f"Primary column '{self.name}' cannot be nullable.")
+            if self.primary and self.type is None:
+                raise ValueError(
+                    f"Primary column '{self.name}' must have a type defined.")
+        return self
+
+
+class TableDefinition(BaseModel):
+    number_of_rows: int
+    partitions: Optional[int] = None
+    columns: List[ColumnDefinition]
+
+
+class DatagenSpec(BaseModel):
+    tables: Dict[str, TableDefinition]
+    output_destination: Optional[Union[UCSchemaTarget, FilePathTarget]] = None
+    generator_options: Optional[Dict[str, Any]] = {}
+    intended_for_databricks: Optional[bool] = None
+
+    @staticmethod
+    def _is_running_on_databricks() -> bool:
+        return "DATABRICKS_RUNTIME_VERSION" in os.environ
+
+    @staticmethod
+    def _determine_path_type(path_prefix: str) -> PathType:
+        path = path_prefix.strip()
+        if len(path.split(".")) == 3:
+            return PathType.UC_TABLE
+        if path.startswith("/Volumes/"):
+            return PathType.UC_VOLUME
+        if path.startswith("/") or path.startswith("\\") or (len(path) > 1 and path[1] == ":"):
+            return PathType.ABSOLUTE_NON_UC
+        return PathType.RELATIVE
+
+    @model_validator(mode="before") 
+    def validate_paths_and_formats(cls, values: Dict[str, Any]) -> Dict[str, Any]:  # noqa: N805, pylint: disable=no-self-argument
+        output = values.get("output_destination")
+        intended = values.get(
+            "intended_for_databricks") or cls._is_running_on_databricks()
+
+        if isinstance(output, FilePathTarget):
+            path_type = cls._determine_path_type(output.base_path)
+            fmt = output.output_format.lower()
+            allowed = {PathType.UC_VOLUME,
+                       PathType.RELATIVE, PathType.ABSOLUTE_NON_UC}
+            if path_type not in allowed:
+                raise ValueError(
+                    f"Invalid path type '{path_type.value}' for FilePathTarget")
+            values["path_type"] = path_type
+
+        elif isinstance(output, UCSchemaTarget):
+            path_type = PathType.UC_TABLE
+            values["path_type"] = path_type
+
+        return values
+
+    def finalize(self) -> None:
+        if self.output_destination is None:
+            raise ValueError(
+                "output_destination must be specified before finalization.")
+
+    def display_all_tables(self):
+        for table_name, table_def in self.tables.items():
+            print(f"Table: {table_name}")
+
+            if self.output_destination:
+                output = f"{self.output_destination}"
+                display(HTML(f"<strong>Output destination:</strong> {output}"))
+            else:
+                message = (
+                    "<strong>Output destination:</strong> "
+                    "<span style='color: red; font-weight: bold;'>None</span><br>"
+                    "<span style='color: gray;'>Set it using the <code>output_destination</code> "
+                    "attribute on your <code>DatagenSpec</code> object "
+                    "(e.g., <code>my_spec.output_destination = UCSchemaTarget(...)</code>).</span>"
+                )
+                display(HTML(message))
+
+            df = pd.DataFrame([col.dict() for col in table_def.columns])
+            try:
+                display(df)
+            except NameError:
+                print(df.to_string())
diff --git a/dbldatagen/extensions/errors.py b/dbldatagen/extensions/errors.py
new file mode 100644
index 00000000..5eb02b4b
--- /dev/null
+++ b/dbldatagen/extensions/errors.py
@@ -0,0 +1,8 @@
+class SpecGenerationError(Exception):
+    """Custom exception for errors during DatagenSpec generation."""
+    pass
+
+
+class DataGenerationError(Exception):
+    """Custom exception for errors during the actual data generation phase."""
+    pass
diff --git a/dbldatagen/extensions/spec_generators/base.py b/dbldatagen/extensions/spec_generators/base.py
new file mode 100644
index 00000000..dc94e850
--- /dev/null
+++ b/dbldatagen/extensions/spec_generators/base.py
@@ -0,0 +1,15 @@
+from abc import ABC, abstractmethod
+from dbldatagen.extensions.datagen_spec import DatagenSpec
+
+
+class AbstractSpecGenerator(ABC):
+    @abstractmethod
+    def generate_spec(self) -> DatagenSpec:
+        """
+        Generates the DatagenSpec from the specific source.
+        Should raise an exception (e.g., SpecGenerationError) if generation fails.
+        """
+        pass
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__}>"
diff --git a/dbldatagen/extensions/spec_generators/employee_generator.py b/dbldatagen/extensions/spec_generators/employee_generator.py
new file mode 100644
index 00000000..8d14c1c5
--- /dev/null
+++ b/dbldatagen/extensions/spec_generators/employee_generator.py
@@ -0,0 +1,120 @@
+from typing import Optional
+from dbldatagen.extensions.datagen_spec import ColumnDefinition, DatagenSpec, TableDefinition
+from dbldatagen.extensions.spec_generators.base import AbstractSpecGenerator
+from dbldatagen.extensions.spec_generators.text_templates import COLUMN_TEMPLATES
+
+
+class EmployeeSpecGenerator(AbstractSpecGenerator):
+    """
+    A static generator for employee dataset that creates a predefined schema
+    with common employee-related fields.
+    """
+
+    def __init__(self, number_of_rows: int = 1000, partitions: Optional[int] = 4):
+        """
+        Initialize the EmployeeSpecGenerator.
+
+        Args:
+            number_of_rows: Number of rows to generate (default: 1000)
+            partitions: Number of partitions for Spark (default: 4)
+        """
+        self.number_of_rows = number_of_rows
+        self.partitions = partitions
+
+    def _get_employee_table_definition(self) -> TableDefinition:
+        """
+        Creates a TableDefinition for the employee dataset with predefined columns.
+        """
+        columns = [
+            ColumnDefinition(name="employee_id", type="int",
+                             primary=True, nullable=False),
+            ColumnDefinition(name="first_name", type="string", options={
+                             "template": COLUMN_TEMPLATES["first_name"]}),
+            ColumnDefinition(name="last_name", type="string", options={
+                             "template": COLUMN_TEMPLATES["last_name"]}),
+            ColumnDefinition(name="email", type="string", options={
+                             "template": COLUMN_TEMPLATES["email"]}),
+            ColumnDefinition(
+                name="department",
+                type="string",
+                options={
+                    "values": [
+                        "Engineering",
+                        "Sales",
+                        "Marketing",
+                        "HR",
+                        "Finance",
+                        "Operations",
+                    ]
+                },
+            ),
+            ColumnDefinition(
+                name="job_title",
+                type="string",
+                options={
+                    "values": [
+                        "Software Engineer",
+                        "Senior Engineer",
+                        "Engineering Manager",
+                        "Sales Representative",
+                        "Sales Manager",
+                        "Marketing Specialist",
+                        "Marketing Manager",
+                        "HR Specialist",
+                        "HR Manager",
+                        "Financial Analyst",
+                        "Finance Manager",
+                        "Operations Specialist",
+                        "Operations Manager",
+                    ]
+                },
+            ),
+            ColumnDefinition(
+                name="salary",
+                type="decimal",
+                options={"min": 50000, "max": 200000, "precision": 2},
+            ),
+            ColumnDefinition(
+                name="hire_date",
+                type="date",
+                options={
+                    "begin": "2020-01-01",
+                    "end": "2024-12-31",
+                    "format": "yyyy-MM-dd",
+                    "random": True,
+                },
+            ),
+            ColumnDefinition(
+                name="is_active",
+                type="boolean",
+                options={
+                    "values": [True, False],
+                    "weights": [9, 1],  # 90% active, 10% inactive
+                },
+            ),
+            ColumnDefinition(
+                name="manager_id",
+                type="int",
+                options={
+                    "baseColumn": "employee_id",
+                    "omit": 0.1,  # 10% chance of being null (no manager)
+                },
+            ),
+        ]
+
+        return TableDefinition(
+            number_of_rows=self.number_of_rows,
+            partitions=self.partitions,
+            columns=columns,
+        )
+
+    def generate_spec(self) -> DatagenSpec:
+        """
+        Generates a DatagenSpec for the employee dataset.
+
+        Returns:
+            DatagenSpec: A specification for generating employee data
+        """
+        employee_table = self._get_employee_table_definition()
+        config_obj = DatagenSpec(tables={"employees": employee_table})
+        return config_obj
diff --git a/dbldatagen/extensions/spec_generators/text_templates.py b/dbldatagen/extensions/spec_generators/text_templates.py
new file mode 100644
index 00000000..5a0c635c
--- /dev/null
+++ b/dbldatagen/extensions/spec_generators/text_templates.py
@@ -0,0 +1,257 @@
+from __future__ import annotations
+import re
+import difflib
+from typing import Dict, Optional, Tuple, Sequence
+from rapidfuzz import process, fuzz
+
+_model = None
+_RX_NON_ALNUM = re.compile(r"[^a-z0-9]+")
+
+COLUMN_TEMPLATES = {
+    # Personal Information
+    "first_name": "\\w",
+    "last_name": "\\w",
+    "surname": "\\w",
+    "full_name": "\\w \\w",
+    "username": "kkk_kkk",
+    "email": "\\w.\\w@\\w.com|\\w@\\w.co.u\\k",
+    "email_alt": "\\w@\\w.com",
+    "password_hash": "xxxxxxxxxxxxxxxx",
+    "phone_number": "(ddd)-ddd-dddd",
+    "mobile_number": "ddd-ddd-dddd",
+    "home_phone": "ddd-ddd-dddd",
+    "dob": "yyyy-mm-dd",
+    "gender": "M|F|O",
+    "ssn": "ddd-dd-dddd",
+    "passport_number": "wwdddddd",
+    "drivers_license": "wwdddddd",
+    "national_id": "dddddddddd",
+    "marital_status": "Single|Married|Divorced|Widowed",
+    "age": "dd",
+    "title": "Mr|Ms|Mrs|Dr|Prof",
+    "middle_name": "\\w",
+    "suffix": "Jr|Sr|II|III|IV",
+    "nickname": "\\w",
+    "birthplace": "\\w",
+    "citizenship": "\\w",
+    "ethnicity": "\\w",
+    "language": "\\w",
+    "religion": "\\w",
+    # Address Information
+    "street_address": "n \\w \\w",
+    "address_line1": "n \\w \\w",
+    "address_line2": "Apt n|Unit n",
+    "city": "\\w",
+    "state": "\\w\\w",
+    "province": "\\w",
+    "region": "\\w",
+    "zip_code": "ddddd",
+    "postal_code": "ddddd",
+    "country": "\\w",
+    "country_code": "ww",
+    "timezone": "www/www",
+    # Business/Employment
+    "company_name": "\\w \\w",
+    "business_name": "\\w \\w",
+    "department": "\\w",
+    "job_title": "\\w \\w",
+    "employee_id": "EMPddddd",
+    "manager_id": "EMPddddd",
+    "hire_date": "yyyy-mm-dd",
+    "termination_date": "yyyy-mm-dd",
+    "salary": "dddddd",
+    "bonus": "ddd",
+    "office_phone": "ddd-ddd-dddd",
+    "work_email": "\\w.\\w@\\w.com",
+    "work_location": "\\w",
+    "supervisor": "\\w \\w",
+    "team": "\\w",
+    "shift": "Morning|Evening|Night",
+    # Financial
+    "credit_card": "dddd-dddd-dddd-dddd",
+    "credit_card_expiry": "mm/yy",
+    "credit_card_cvv": "ddd",
+    "account_number": "dddddddddd",
+    "routing_number": "dddddddd",
+    "iban": "wwdddddddddddddddddddddd",
+    "swift_code": "wwwwwwww",
+    "tax_id": "dd-dddddddd",
+    "bank_name": "\\w \\w",
+    "balance": "dddddd",
+    "currency": "www",
+    # Capital Markets
+    "ticker": "WWW|WWWW|WWWWW",
+    "isin": "WWdddddddddd",
+    "cusip": "Wdddddddd",
+    "sedol": "dWWWWdd",
+    "figi": "WWWWWWWWWWWWWW",
+    "ric": "WWWW.WW",
+    "exchange_code": "WWWW",
+    "security_id": "SECdddddd",
+    "instrument_id": "INSdddddd",
+    "asset_class": "Equity|Bond|FX|Commodity|Derivative",
+    "security_type": "Stock|Bond|Option|Future|ETF|ADR|GDR",
+    "market_cap": "dddddddddd",
+    "shares_outstanding": "dddddddddd",
+    "price": "ddd.dd",
+    "volume": "dddddddd",
+    "bid_price": "ddd.dd",
+    "ask_price": "ddd.dd",
+    "last_trade_price": "ddd.dd",
+    "open_price": "ddd.dd",
+    "close_price": "ddd.dd",
+    "high_price": "ddd.dd",
+    "low_price": "ddd.dd",
+    "trade_id": "TRDdddddd",
+    "order_id": "ORDdddddd",
+    "execution_id": "EXCdddddd",
+    "portfolio_id": "PRTdddddd",
+    "fund_id": "FNDdddddd",
+    "index_id": "IDXdddddd",
+    "option_id": "OPTdddddd",
+    "future_id": "FUTdddddd",
+    "bond_id": "BNDdddddd",
+    "etf_id": "ETFdddddd",
+    "mutual_fund_id": "MFdddddd",
+    # Product/Inventory
+    "product_code": "PRDddddd",
+    "sku": "SKUddddd",
+    "upc": "dddddddddddd",
+    "ean": "ddddddddddddd",
+    "isbn": "d-ddd-ddddd-d",
+    "model_number": "MDLddddd",
+    "serial_number": "SNdddddddd",
+    # System/Network
+    "ip_address": "n.n.n.n",
+    "mac_address": "xx:xx:xx:xx:xx:xx",
+    "hostname": "kkkkkk",
+    "domain": "\\w.com|\\w.net|\\w.org",
+    "url": "https://\\w.\\w.com/\\w",
+    "api_key": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
+    "session_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
+    "token": "xxxxxxxxxxxxxxxx",
+    "device_id": "DEVddddd",
+    "machine_id": "MCHddddd",
+    "server_id": "SVRddddd",
+    "cluster_id": "CLSddddd",
+    "node_id": "NODddddd",
+    # Document/File
+    "document_id": "DOCddddd",
+    "file_name": "\\w.\\w",
+    "file_path": "/\\w/\\w/\\w.\\w",
+    "mime_type": "\\w/\\w",
+    "file_extension": ".\\w",
+    # Location/Geo
+    "latitude": "dd.dddddd",
+    "longitude": "ddd.dddddd",
+    "gps_coordinates": "dd.dddddd, ddd.dddddd",
+    # Social Media
+    "twitter_handle": "@\\w",
+    "instagram_username": "@\\w",
+    "facebook_id": "dddddddddd",
+    "linkedin_id": "\\w-\\w-\\w",
+    # Miscellaneous
+    "status": "Active|Inactive|Pending|Closed",
+    "priority": "High|Medium|Low",
+    "category": "\\w",
+    "type": "\\w",
+    "description": "\\w \\w \\w",
+    "notes": "\\w \\w \\w",
+    "comments": "\\w \\w \\w",
+    "reference": "REFddddd",
+    "code": "dddddd",
+    "flag": "Y|N",
+    "is_active": "true|false",
+    "created_at": "yyyy-mm-dd hh:mm:ss",
+    "updated_at": "yyyy-mm-dd hh:mm:ss",
+    "timestamp": "yyyy-mm-dd hh:mm:ss",
+}
+
+
+# def _semantic_match(name: str, candidates: Tuple[str, ...]) -> Tuple[str, float]:
+#     """Transformer‑based semantic similarity (0‑1)."""
+#     if not _model:
+#         return "", 0.0
+#     emb_query = _model.encode(name, normalize_embeddings=True)
+#     emb_cand = _model.encode(list(candidates), normalize_embeddings=True)
+#     sims = util.cos_sim(emb_query, emb_cand)[0].tolist()
+#     idx = int(max(range(len(sims)), key=sims.__getitem__))
+#     return candidates[idx], sims[idx]
+
+
+def _normalize(s: str) -> str:
+    """Lower‑case, strip accents (if any), collapse to ASCII letters+digits."""
+    s = s.lower()
+    s = _RX_NON_ALNUM.sub("_", s)
+    return re.sub(r"_{2,}", "_", s).strip("_")
+
+
+def _fuzzy_match(name: str, candidates: Sequence[str]) -> Tuple[str, float]:
+    """
+    Return the best fuzzy match among *candidates* for *name* and the score
+    on a 0‑1 scale.  If nothing passes, returns ("", 0.0).
+    """
+    # ---- RapidFuzz branch --------------------------------------------------
+    try:
+        result = process.extractOne(
+            name,
+            list(candidates),  # cast to list for the stub
+            scorer=fuzz.token_sort_ratio,  # kw‑only param -> fine
+        )
+        if result is not None:
+            best, score, _ = result  # result = (match, score, idx)
+            return best, score / 100.0
+    except ImportError:
+        pass
+
+    # ---- difflib fallback --------------------------------------------------
+    best = difflib.get_close_matches(name, candidates, n=1, cutoff=0)
+    if best:
+        score = difflib.SequenceMatcher(None, name, best[0]).ratio()
+        return best[0], score
+
+    return "", 0.0
+
+
+def guess_template(col_name: str) -> Optional[str]:
+    """
+    Given an *original* column name from a database, return the most relevant
+    synthetic‑data template (regex / string) from ``column_templates``.
+    If no reasonable match is found, return ``None``.
+
+    Advanced matching stack:
+    1.  Exact key      → O(1)
+    2.  Alias map      → O(1)
+    3.  Fuzzy match    → rapidfuzz or difflib
+    4.  Semantic match → sentence‑transformers
+    """
+    if not col_name:
+        return None
+    norm = _normalize(col_name)
+    # exact match
+    if norm in COLUMN_TEMPLATES:
+        return COLUMN_TEMPLATES[norm]
+
+    # # alias table
+    # if norm in ALIASES:
+    #     return column_templates[ALIASES[norm]]
+
+    # candidate keys to consider in later steps
+    keys = tuple(COLUMN_TEMPLATES.keys())
+
+    # fuzzy distance
+    best_fuzzy, fuzzy_score = _fuzzy_match(norm, keys)
+    if fuzzy_score >= 0.9:  # very high confidence
+        return COLUMN_TEMPLATES[best_fuzzy]
+
+    # # TODO: semantic similarity
+    # best_sem, sem_score = _semantic_match(col_name, keys)
+    # if sem_score >= 0.6 and sem_score > fuzzy_score:
+    #     return COLUMN_TEMPLATES[best_sem]
+
+    # lower‑threshold fuzzy fallback
+    if fuzzy_score >= 0.75:
+        return COLUMN_TEMPLATES[best_fuzzy]
+
+    # Nothing good enough
+    return None
diff --git a/dbldatagen/extensions/spec_generators/uc_generator.py b/dbldatagen/extensions/spec_generators/uc_generator.py
new file mode 100644
index 00000000..9a7dc9d3
--- /dev/null
+++ b/dbldatagen/extensions/spec_generators/uc_generator.py
@@ -0,0 +1,298 @@
+from typing import List, Dict, Optional
+import json
+import logging
+from pydantic import ValidationError
+from databricks.sdk import WorkspaceClient
+from databricks.sdk.service.catalog import TableInfo, ColumnInfo, TableConstraint
+from dbldatagen.extensions.errors import SpecGenerationError
+from dbldatagen.extensions.datagen_spec import (
+    ColumnDefinition,
+    DatagenSpec,
+    TableDefinition,
+    DbldatagenBasicType,
+)
+
+from dbldatagen.extensions.spec_generators.base import AbstractSpecGenerator
+from dbldatagen.extensions.spec_generators.text_templates import guess_template
+
+# Set up logger
+logger = logging.getLogger(__name__)
+
+
+# --- User's custom generator ---
+class DatabricksUCSpecGenerator(AbstractSpecGenerator):
+    def __init__(self, tables: List[str], workspace_client: Optional[WorkspaceClient] = None):
+        try:
+            self.workspace_client = workspace_client or WorkspaceClient()
+        except Exception as e:
+            # Catch potential errors during WorkspaceClient instantiation (e.g., config issues)
+            raise SpecGenerationError(
+                f"Failed to initialize WorkspaceClient: {e}") from e
+        if not tables:
+            raise SpecGenerationError("Fully qualified table name required")
+        self.tables = tables
+
+    @staticmethod
+    def _map_source_type_to_datagen_type(
+        source_type_str: Optional[str], column_name: str = "Unknown"
+    ) -> DbldatagenBasicType:
+        """
+        Maps a source data type string to a DbldatagenBasicType.
+
+        Args:
+            source_type_str: The source data type string (e.g., from UC schema).
+            column_name: The name of the column, for logging purposes.
+
+        Returns:
+            The corresponding DbldatagenBasicType.
+
+        Raises:
+            SchemaParsingError: If the source type cannot be reliably mapped.
+        """
+        if not source_type_str:
+            logger.warning(
+                f"Warning: Source type for column '{column_name}' is None or empty. "
+                f"Defaulting to 'string'.")
+            return "string"
+
+        s_type = source_type_str.lower().strip()
+
+        # Direct matches with DbldatagenBasicType (assuming DbldatagenBasicType is a Literal or Enum)
+        # For Literal, we need to check against its __args__
+        if hasattr(DbldatagenBasicType, "__args__") and s_type in DbldatagenBasicType.__args__:  # type: ignore
+            return s_type  # type: ignore
+
+        # Handle common variations or alternative names from catalogs
+        type_mapping: Dict[str, DbldatagenBasicType] = {
+            "int": "integer",
+            "integer": "integer",
+            "bigint": "long",
+            "smallint": "short",
+            "tinyint": "byte",
+            "varchar": "string",
+            "char": "string",
+            "text": "string",
+            "character varying": "string",
+            "numeric": "decimal",
+            "number": "decimal",
+            "dec": "decimal",
+            "double precision": "double",
+            "real": "float",
+            "bool": "boolean",
+            "timestamp_ntz": "timestamp",  # Specific handling for Databricks
+            "timestamp with local time zone": "timestamp",
+            "timestamp without time zone": "timestamp",
+            "date": "date",
+            "binary": "binary",
+            "varbinary": "binary",
+        }
+
+        # Check direct mapping first
+        if s_type in type_mapping:
+            return type_mapping[s_type]
+
+        # Handle types with parameters like decimal(p,s) or varchar(n)
+        if s_type.startswith(("decimal", "numeric")):
+            return "decimal"
+        if s_type.startswith(("varchar", "char")):
+            return "string"
+
+        # Fallback or raise error if mapping is critical
+        # Consider making this stricter by raising an error if a type is truly unknown
+        logger.warning(
+            f"Warning: Unmapped source type '{source_type_str}' for column '{column_name}'. "
+            f"Defaulting to 'string'.")
+        return "string"
+
+    def _generate_column_definitions(
+        self,
+        raw_column_infos: List[ColumnInfo],
+        primary_key_column_names: List[str],
+    ) -> List[ColumnDefinition]:
+        """
+        Generates a list of Pydantic ColumnDefinition objects from raw column schema information.
+
+        Args:
+            raw_column_infos: A list of ColumnInfo objects from Databricks SDK.
+            primary_key_column_names: A list of column names that are part of the primary key.
+
+        Returns:
+            A list of ColumnDefinition objects.
+
+        Raises:
+            SchemaParsingError: If essential column information is missing or invalid.
+        """
+        pydantic_column_definitions: List[ColumnDefinition] = []
+
+        if not raw_column_infos:
+            raise SpecGenerationError(
+                "_generate_column_definitions: No column information provided.")
+
+        for raw_col_info in raw_column_infos:
+            col_name = getattr(raw_col_info, "name", None)
+            if not col_name:
+                # Or raise SchemaParsingError
+                logger.warning("Warning: Skipping column info object without a 'name' attribute.")
+                continue
+
+            source_type_str: Optional[str] = None
+            type_json_str = getattr(raw_col_info, "type_json", None)
+
+            if type_json_str:
+                try:
+                    type_info_dict = json.loads(type_json_str)
+                    source_type_str = type_info_dict.get("type")
+                except json.JSONDecodeError:
+                    logger.warning(
+                        f"Warning: Could not parse type_json for column '{col_name}'. "
+                        f"Falling back to type_name/type_text."
+                    )
+
+            if not source_type_str:  # Fallback to type_name or type_text
+                source_type_str = getattr(raw_col_info, "type_name", None)
+            if not source_type_str:  # Further fallback
+                # Default to string if all else fails
+                source_type_str = getattr(raw_col_info, "type_text", None)
+
+            if not source_type_str:
+                logger.warning(
+                    f"Warning: Could not determine source type for column '{col_name}'. Defaulting to 'string'.")
+                source_type_str = "string"
+
+            datagen_col_type = self._map_source_type_to_datagen_type(
+                source_type_str, col_name)
+
+            is_primary = col_name in primary_key_column_names
+
+            is_nullable_from_source = getattr(raw_col_info, "nullable", True)
+            if isinstance(is_nullable_from_source, str):
+                is_nullable_from_source = is_nullable_from_source.lower() == "true"
+
+            # Primary keys cannot be nullable.
+            final_nullable = False if is_primary else bool(
+                is_nullable_from_source)
+
+            col_data = {
+                "name": col_name,
+                "type": datagen_col_type,
+                "primary": is_primary,
+                "nullable": final_nullable,
+            }
+
+            if datagen_col_type == "decimal":
+                precision = getattr(raw_col_info, "type_precision", None)
+                scale = getattr(raw_col_info, "type_scale", None)
+
+                # Try to parse from type_json if not directly available
+                if precision is None and type_json_str:
+                    try:
+                        type_info_dict = json.loads(type_json_str)
+                        precision = type_info_dict.get("precision")
+                    except (json.JSONDecodeError, TypeError):
+                        pass  # Ignore if parsing fails or precision not found
+
+                if scale is None and type_json_str:
+                    try:
+                        type_info_dict = json.loads(type_json_str)
+                        scale = type_info_dict.get("scale")
+                    except (json.JSONDecodeError, TypeError):
+                        pass  # Ignore if parsing fails or scale not found
+
+                if precision is not None:
+                    col_data["precision"] = int(precision)
+                if scale is not None:
+                    col_data["scale"] = int(scale)
+
+            elif datagen_col_type == "string" and not is_primary:
+                template = guess_template(col_name)
+                if template:
+                    col_data["options"] = {"template": template}
+
+            try:
+                column_def = ColumnDefinition(**col_data)  # type: ignore
+                pydantic_column_definitions.append(column_def)
+            except ValidationError as e:
+                raise SpecGenerationError(
+                    f"Validation error for column '{col_name}': {e}") from e
+
+        return pydantic_column_definitions
+
+    def _get_table_definition_from_uc(
+        self,
+        uc_table_full_name: str,
+        default_number_of_rows: int = 1000,
+        default_partitions: Optional[int] = 1,
+    ) -> TableDefinition:
+        """
+        Retrieves table schema from Unity Catalog and generates a TableDefinition.
+
+        Args:
+            uc_table_full_name: The three-part name of the table in Unity Catalog
+                                (e.g., "catalog_name.schema_name.table_name").
+            default_number_of_rows: Default number of rows for the TableDefinition.
+            default_partitions: Default number of partitions.
+
+        Returns:
+            A TableDefinition object.
+
+        Raises:
+            WorkspaceError: If the table cannot be found or accessed.
+            SchemaParsingError: If schema information is incomplete or invalid.
+        """
+        try:
+            table_info: TableInfo = self.workspace_client.tables.get(
+                uc_table_full_name)
+        except Exception as e:  # Catching generic exception from SDK
+            raise SpecGenerationError(
+                f"Error fetching table '{uc_table_full_name}' from Unity Catalog: {e}") from e
+
+        if not table_info:
+            raise SpecGenerationError(
+                f"Table '{uc_table_full_name}' not found in Unity Catalog.")
+
+        if not table_info.columns:
+            raise SpecGenerationError(
+                f"No columns found for table '{uc_table_full_name}'.")
+
+        primary_key_column_names: List[str] = []
+        if table_info.table_constraints:
+            for constraint in table_info.table_constraints:
+                # Ensure constraint and primary_key_constraint are not None
+                if constraint and constraint.primary_key_constraint:
+                    # Ensure child_columns is not None and is iterable
+                    if constraint.primary_key_constraint.child_columns:
+                        primary_key_column_names.extend(
+                            constraint.primary_key_constraint.child_columns)
+
+        column_definitions = self._generate_column_definitions(
+            raw_column_infos=table_info.columns,
+            primary_key_column_names=list(
+                set(primary_key_column_names)),  # Ensure uniqueness
+        )
+
+        try:
+            table_def = TableDefinition(
+                number_of_rows=default_number_of_rows,
+                partitions=default_partitions,
+                columns=column_definitions,
+            )
+            return table_def
+        except ValidationError as e:
+            raise SpecGenerationError(
+                f"Validation error creating TableDefinition for '{uc_table_full_name}': {e}"
+            ) from e
+
+    def generate_spec(self) -> DatagenSpec:
+        tables_def = {}
+        for table in self.tables:
+            table_definition_model: TableDefinition = self._get_table_definition_from_uc(
+                uc_table_full_name=table,
+                default_number_of_rows=5000,
+                default_partitions=4,
+            )
+            table_name = table.split(".")[-1]
+            tables_def[table_name] = table_definition_model
+
+        # Note: The destination is left empty so that it does not confuse the user. Find a way to notify user of that
+        config_obj = DatagenSpec(tables=tables_def)
+        return config_obj
diff --git a/pytest.ini b/pytest.ini
index 5ac0c440..32c939c6 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -5,3 +5,7 @@ log_cli_level = INFO
 log_cli_format = [pytest][%(asctime)s][%(levelname)s][%(module)s][%(funcName)s] %(message)s
 log_cli_date_format = %Y-%m-%d %H:%M:%S
 log_level = INFO
+
+
+markers =
+    extensions: marks tests as extension tests
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 7ee9eeff..9ae75245 100644
--- a/setup.py
+++ b/setup.py
@@ -29,6 +29,16 @@
     benchmarking, environment validation and other purposes.
     """
 
+extras_require = {
+    'extension': [
+        'pydantic>=2.0.0',
+        "rapidfuzz>=3.13.0"  # Only pydantic as additional dependency
+    ],
+    'spec-generators': [
+        'databricks-sdk==0.62.0'
+    ]
+}
+
 setuptools.setup(
     name="dbldatagen",
     version="0.4.0post2",
@@ -45,7 +55,8 @@
     packages=['dbldatagen',
               'dbldatagen.distributions',
               'dbldatagen.constraints',
-              'dbldatagen.datasets'
+              'dbldatagen.datasets',
+              'dbldatagen.extensions'
               ],
     license="Databricks License",
     classifiers=[
@@ -56,4 +67,5 @@
         "Intended Audience :: System Administrators"
     ],
     python_requires='>=3.10.12',
+    extras_require=extras_require,
 )
diff --git a/tests/test_dataspec_generator.py b/tests/test_dataspec_generator.py
new file mode 100644
index 00000000..4fff49e7
--- /dev/null
+++ b/tests/test_dataspec_generator.py
@@ -0,0 +1,193 @@
+# from unittest.mock import MagicMock, patch
+# import pytest
+# from pydantic import ValidationError
+# from pyspark.sql import SparkSession
+#
+# from dbldatagen.extensions.datagen_spec import (
+#     DatagenSpec,
+#     TableDefinition,
+#     ColumnDefinition,
+#     FilePathTarget,
+# )
+#
+# from dbldatagen.extensions.data_generator_from_spec import Generator
+#
+#
+# @pytest.fixture
+# def spark_session():
+#     """Fixture to create a SparkSession for testing."""
+#     return SparkSession.builder.appName("TestDataGen").master("local[2]").getOrCreate()
+#
+#
+# @pytest.fixture
+# def sample_config():
+#     """Fixture to create a sample DatagenSpec for testing."""
+#     return DatagenSpec(
+#         tables={
+#             "test_table": TableDefinition(
+#                 number_of_rows=100,
+#                 partitions=2,
+#                 columns=[
+#                     ColumnDefinition(name="id", type="string", primary=True),
+#                     ColumnDefinition(name="value", type="integer", options={
+#                                      "min": 1, "max": 100}),
+#                 ],
+#             )
+#         },
+#         output_destination=FilePathTarget(
+#             base_path="/Volume/testing",
+#             output_format="parquet",
+#         ),
+#     )
+#
+#
+# def test_init_with_spark(spark_session):
+#     """Test Generator initialization with provided SparkSession."""
+#     generator = Generator(spark=spark_session)
+#     assert generator.spark == spark_session
+#
+#
+# def test_init_without_spark():
+#     """Test Generator initialization without SparkSession."""
+#     with pytest.raises(RuntimeError, match="SparkSession cannot be None"):
+#         Generator(spark=None)
+#
+#
+# def test_prepare_data_generators(spark_session, sample_config):
+#     """Test preparing data generators from config."""
+#     generator = Generator(spark=spark_session)
+#     prepared_generators = generator.prepare_data_generators(sample_config)
+#
+#     assert "test_table" in prepared_generators
+#     assert prepared_generators["test_table"] is not None
+#     assert prepared_generators["test_table"].rowCount == 100
+#
+#
+# def test_prepare_data_generators_with_invalid_config(spark_session):
+#     """Test preparing data generators with invalid config."""
+#     generator = Generator(spark=spark_session)
+#
+#     with pytest.raises(ValidationError, match="Value error, Primary column 'id' must have a type defined"):
+#         invalid_config = DatagenSpec(
+#             tables={
+#                 "test_table": TableDefinition(
+#                     number_of_rows=100,
+#                     partitions=2,
+#                     columns=[
+#                         ColumnDefinition(
+#                             name="id",
+#                             primary=True,  # Missing type for primary key
+#                         )
+#                     ],
+#                 )
+#             },
+#             output_destination=FilePathTarget(
+#                 base_path="/Volume/test_output",
+#                 output_format="parquet",
+#             ),
+#         )
+#
+#
+# def test_creating_column_definition_with_nullable_primary_key_fails():
+#     """
+#     Tests that attempting to create a ColumnDefinition instance with
+#     primary=True and nullable=True raises a Pydantic ValidationError.
+#     """
+#     with pytest.raises(ValidationError, match="Value error, Primary column 'id' cannot be nullable"):
+#         # This is the action that should trigger the Pydantic validation error
+#         ColumnDefinition(
+#             name="id",
+#             type="string",
+#             primary=True,
+#             nullable=True,  # This combination is invalid by your model's rules
+#         )
+#
+#
+# # Alternatively, if you want to test it through the DatagenSpec creation:
+# def test_creating_datagenspec_with_nullable_primary_key_fails():
+#     """
+#     Tests that attempting to create a DatagenSpec instance containing
+#     a nullable primary key raises a Pydantic ValidationError.
+#     """
+#     with pytest.raises(ValidationError, match="Value error, Primary column 'id' cannot be nullable"):
+#         DatagenSpec(
+#             tables={
+#                 "test_table": TableDefinition(
+#                     number_of_rows=100,
+#                     partitions=2,
+#                     columns=[
+#                         ColumnDefinition(  # This is the invalid part
+#                             name="id", type="string", primary=True, nullable=True
+#                         )
+#                     ],
+#                 )
+#             },
+#             output_format="parquet",
+#             output_path_prefix="/Volume/test_output",
+#         )
+#
+#
+# @patch("dbldatagen.extensions.data_generator_from_spec.Generator.write_prepared_data")
+# def test_generate_and_write_data(mock_write, spark_session, sample_config):
+#     """Test combined generation and writing of data."""
+#     generator = Generator(spark=spark_session)
+#     mock_write.return_value = True
+#
+#     result = generator.generate_and_write_data(sample_config)
+#     assert result is True
+#     mock_write.assert_called_once()
+#
+#
+# def test_write_prepared_data(spark_session, sample_config):
+#     """Test writing prepared data to output."""
+#     generator = Generator(spark=spark_session)
+#     prepared_generators = generator.prepare_data_generators(sample_config)
+#
+#     # Mock the DataFrame write operation
+#     with patch("pyspark.sql.DataFrame.write") as mock_write:
+#         mock_write.format.return_value = mock_write
+#         mock_write.mode.return_value = mock_write
+#         mock_write.save.return_value = None
+#
+#         result = generator.write_prepared_data(
+#             prepared_generators=prepared_generators,
+#             output_destination=sample_config.output_destination,
+#         )
+#
+#         assert result is True
+#         # mock_write.format.assert_called_with(sample_config.output_format)
+#         # mock_write.mode.assert_called_with("overwrite")
+#         # mock_write.save.assert_called()
+#
+#
+# def test_write_prepared_data_with_empty_generators(spark_session):
+#     """Test writing with empty prepared generators."""
+#     generator = Generator(spark=spark_session)
+#     result = generator.write_prepared_data({}, "parquet", "/tmp/test_output")
+#     assert result is True  # Should return True as there's nothing to write
+#
+#
+# def test_write_prepared_data_with_failed_generator(spark_session, sample_config):
+#     """Test writing with a failed generator (None value)."""
+#     generator = Generator(spark=spark_session)
+#     prepared_generators = {"test_table": None}  # Simulate a failed generator
+#
+#     result = generator.write_prepared_data(prepared_generators, output_destination=sample_config.output_destination)
+#     assert result is True  # Should return True as we skip failed generators
+#
+#
+# def test_write_prepared_data_with_write_error(spark_session, sample_config):
+#     """Test writing with a DataFrame write error."""
+#     generator = Generator(spark=spark_session)
+#     prepared_generators, _ = generator.prepare_data_generators(sample_config)
+#
+#     # Mock the DataFrame write operation to raise an error
+#     with patch("pyspark.sql.DataFrame.write") as mock_write:
+#         mock_write.format.return_value = mock_write
+#         mock_write.mode.return_value = mock_write
+#         mock_write.save.side_effect = Exception("Write error")
+#
+#         result = generator.write_prepared_data(
+#             prepared_generators, output_destination=sample_config.output_destination)
+#
+#         assert result is False  # Should return False on write error
diff --git a/tests/test_spec.py b/tests/test_spec.py
new file mode 100644
index 00000000..12c85df5
--- /dev/null
+++ b/tests/test_spec.py
@@ -0,0 +1,466 @@
+from dbldatagen.spec.generator_spec import DatagenSpec
+import pytest
+from dbldatagen.spec.generator_spec import (
+    DatagenSpec,
+    TableDefinition,
+    ColumnDefinition,
+    UCSchemaTarget,
+    FilePathTarget,
+    ValidationResult
+)
+
+class TestValidationResult:
+    """Tests for ValidationResult class"""
+
+    def test_empty_result_is_valid(self):
+        result = ValidationResult()
+        assert result.is_valid()
+        assert len(result.errors) == 0
+        assert len(result.warnings) == 0
+
+    def test_result_with_errors_is_invalid(self):
+        result = ValidationResult()
+        result.add_error("Test error")
+        assert not result.is_valid()
+        assert len(result.errors) == 1
+
+    def test_result_with_only_warnings_is_valid(self):
+        result = ValidationResult()
+        result.add_warning("Test warning")
+        assert result.is_valid()
+        assert len(result.warnings) == 1
+
+    def test_result_string_representation(self):
+        result = ValidationResult()
+        result.add_error("Error 1")
+        result.add_error("Error 2")
+        result.add_warning("Warning 1")
+
+        result_str = str(result)
+        assert "✗ Validation failed" in result_str
+        assert "Errors (2)" in result_str
+        assert "Error 1" in result_str
+        assert "Error 2" in result_str
+        assert "Warnings (1)" in result_str
+        assert "Warning 1" in result_str
+
+    def test_valid_result_string_representation(self):
+        result = ValidationResult()
+        result_str = str(result)
+        assert "✓ Validation passed successfully" in result_str
+
+
+class TestColumnDefinitionValidation:
+    """Tests for ColumnDefinition validation"""
+
+    def test_valid_primary_column(self):
+        col = ColumnDefinition(
+            name="id",
+            type="int",
+            primary=True
+        )
+        assert col.primary
+        assert col.type == "int"
+
+    def test_primary_column_with_min_max_raises_error(self):
+        with pytest.raises(ValueError, match="cannot have min/max options"):
+            ColumnDefinition(
+                name="id",
+                type="int",
+                primary=True,
+                options={"min": 1, "max": 100}
+            )
+
+    def test_primary_column_nullable_raises_error(self):
+        with pytest.raises(ValueError, match="cannot be nullable"):
+            ColumnDefinition(
+                name="id",
+                type="int",
+                primary=True,
+                nullable=True
+            )
+
+    def test_primary_column_without_type_raises_error(self):
+        with pytest.raises(ValueError, match="must have a type defined"):
+            ColumnDefinition(
+                name="id",
+                primary=True
+            )
+
+    def test_non_primary_column_without_type(self):
+        # Should not raise
+        col = ColumnDefinition(
+            name="data",
+            options={"values": ["a", "b", "c"]}
+        )
+        assert col.name == "data"
+
+
+class TestDatagenSpecValidation:
+    """Tests for DatagenSpec.validate() method"""
+
+    def test_valid_spec_passes_validation(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="name", type="string", options={"values": ["Alice", "Bob"]}),
+                    ]
+                )
+            },
+            output_destination=UCSchemaTarget(catalog="main", schema_="default")
+        )
+
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+        assert len(result.errors) == 0
+
+    def test_empty_tables_raises_error(self):
+        spec = DatagenSpec(tables={})
+
+        with pytest.raises(ValueError, match="at least one table"):
+            spec.validate(strict=True)
+
+    def test_table_without_columns_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "empty_table": TableDefinition(
+                    number_of_rows=100,
+                    columns=[]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="must have at least one column"):
+            spec.validate()
+
+    def test_negative_row_count_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=-10,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="invalid number_of_rows"):
+            spec.validate()
+
+    def test_zero_row_count_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=0,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="invalid number_of_rows"):
+            spec.validate()
+
+    def test_invalid_partitions_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    partitions=-5,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="invalid partitions"):
+            spec.validate()
+
+    def test_duplicate_column_names_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="duplicate", type="string"),
+                        ColumnDefinition(name="duplicate", type="int"),
+                    ]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="duplicate column names"):
+            spec.validate()
+
+    def test_invalid_base_column_reference_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="email", type="string", baseColumn="nonexistent"),
+                    ]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="does not exist"):
+            spec.validate()
+
+    def test_circular_dependency_raises_error(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="col_a", type="string", baseColumn="col_b"),
+                        ColumnDefinition(name="col_b", type="string", baseColumn="col_c"),
+                        ColumnDefinition(name="col_c", type="string", baseColumn="col_a"),
+                    ]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError, match="Circular dependency"):
+            spec.validate()
+
+    def test_multiple_primary_columns_warning(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id1", type="int", primary=True),
+                        ColumnDefinition(name="id2", type="int", primary=True),
+                    ]
+                )
+            }
+        )
+
+        # In strict mode, warnings cause errors
+        with pytest.raises(ValueError, match="multiple primary columns"):
+            spec.validate(strict=True)
+
+        # In non-strict mode, should pass but have warnings
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+        assert len(result.warnings) > 0
+        assert any("multiple primary columns" in w for w in result.warnings)
+
+    def test_column_without_type_or_options_warning(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="empty_col"),
+                    ]
+                )
+            }
+        )
+
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+        assert len(result.warnings) > 0
+        assert any("No type specified" in w for w in result.warnings)
+
+    def test_no_output_destination_warning(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                )
+            }
+        )
+
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+        assert len(result.warnings) > 0
+        assert any("No output_destination" in w for w in result.warnings)
+
+    def test_unknown_generator_option_warning(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                )
+            },
+            generator_options={"unknown_option": "value"}
+        )
+
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+        assert len(result.warnings) > 0
+        assert any("Unknown generator option" in w for w in result.warnings)
+
+    def test_multiple_errors_collected(self):
+        """Test that all errors are collected before raising"""
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=-10,  # Error 1
+                    partitions=0,  # Error 2
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="id", type="string"),  # Error 3: duplicate
+                        ColumnDefinition(name="email", baseColumn="phone"),  # Error 4: nonexistent
+                    ]
+                )
+            }
+        )
+
+        with pytest.raises(ValueError) as exc_info:
+            spec.validate()
+
+        error_msg = str(exc_info.value)
+        # Should contain all errors
+        assert "invalid number_of_rows" in error_msg
+        assert "invalid partitions" in error_msg
+        assert "duplicate column names" in error_msg
+        assert "does not exist" in error_msg
+
+    def test_strict_mode_raises_on_warnings(self):
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                )
+            }
+            # No output_destination - will generate warning
+        )
+
+        # Strict mode should raise
+        with pytest.raises(ValueError):
+            spec.validate(strict=True)
+
+        # Non-strict mode should pass
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+
+    def test_valid_base_column_chain(self):
+        """Test that valid baseColumn chains work"""
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[
+                        ColumnDefinition(name="id", type="int", primary=True),
+                        ColumnDefinition(name="code", type="string", baseColumn="id"),
+                        ColumnDefinition(name="hash", type="string", baseColumn="code"),
+                    ]
+                )
+            },
+            output_destination=FilePathTarget(base_path="/tmp/data", output_format="parquet")
+        )
+
+        result = spec.validate(strict=False)
+        assert result.is_valid()
+
+    def test_multiple_tables_validation(self):
+        """Test validation across multiple tables"""
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=100,
+                    columns=[ColumnDefinition(name="id", type="int", primary=True)]
+                ),
+                "orders": TableDefinition(
+                    number_of_rows=-50,  # Error in second table
+                    columns=[ColumnDefinition(name="order_id", type="int", primary=True)]
+                ),
+                "products": TableDefinition(
+                    number_of_rows=200,
+                    columns=[]  # Error: no columns
+                )
+            }
+        )
+
+        with pytest.raises(ValueError) as exc_info:
+            spec.validate()
+
+        error_msg = str(exc_info.value)
+        # Should find errors in both tables
+        assert "orders" in error_msg
+        assert "products" in error_msg
+
+
+class TestTargetValidation:
+    """Tests for output target validation"""
+
+    def test_valid_uc_schema_target(self):
+        target = UCSchemaTarget(catalog="main", schema_="default")
+        assert target.catalog == "main"
+        assert target.schema_ == "default"
+
+    def test_uc_schema_empty_catalog_raises_error(self):
+        with pytest.raises(ValueError, match="non-empty"):
+            UCSchemaTarget(catalog="", schema_="default")
+
+    def test_valid_file_path_target(self):
+        target = FilePathTarget(base_path="/tmp/data", output_format="parquet")
+        assert target.base_path == "/tmp/data"
+        assert target.output_format == "parquet"
+
+    def test_file_path_empty_base_path_raises_error(self):
+        with pytest.raises(ValueError, match="non-empty"):
+            FilePathTarget(base_path="", output_format="csv")
+
+    def test_file_path_invalid_format_raises_error(self):
+        with pytest.raises(ValueError):
+            FilePathTarget(base_path="/tmp/data", output_format="json")
+
+
+class TestValidationIntegration:
+    """Integration tests for validation"""
+
+    def test_realistic_valid_spec(self):
+        """Test a realistic, valid specification"""
+        spec = DatagenSpec(
+            tables={
+                "users": TableDefinition(
+                    number_of_rows=1000,
+                    partitions=4,
+                    columns=[
+                        ColumnDefinition(name="user_id", type="int", primary=True),
+                        ColumnDefinition(name="username", type="string", options={
+                            "template": r"\w{8,12}"
+                        }),
+                        ColumnDefinition(name="email", type="string", options={
+                            "template": r"\w.\w@\w.com"
+                        }),
+                        ColumnDefinition(name="age", type="int", options={
+                            "min": 18, "max": 99
+                        }),
+                    ]
+                ),
+                "orders": TableDefinition(
+                    number_of_rows=5000,
+                    columns=[
+                        ColumnDefinition(name="order_id", type="int", primary=True),
+                        ColumnDefinition(name="amount", type="decimal", options={
+                            "min": 10.0, "max": 1000.0
+                        }),
+                    ]
+                )
+            },
+            output_destination=UCSchemaTarget(
+                catalog="main",
+                schema_="synthetic_data"
+            ),
+            generator_options={
+                "random": True,
+                "randomSeed": 42
+            }
+        )
+
+        result = spec.validate(strict=True)
+        assert result.is_valid()
+        assert len(result.errors) == 0
+        assert len(result.warnings) == 0