|
6 | 6 | "source": [ |
7 | 7 | "# Open data studio\n", |
8 | 8 | "\n", |
9 | | - "[Open data studio](https://open-datastudio.io) is a managed computing computing service on [Staroid](https://staroid.com) cloud. Run your machine learning and large scale data processing workloads without managing clusters and servers.\n", |
| 9 | + "[Open data studio](https://open-datastudio.io) is a managed computing service on [Staroid](https://staroid.com). Run your machine learning and large scale data processing workloads without managing clusters and servers.\n", |
10 | 10 | "\n", |
11 | | - "Supported computing frameworks are\n", |
| 11 | + "[ods](https://github.com/open-datastudio/ods) library makes it easy to use in a Python environment. Currently, the library supports the following computing frameworks.\n", |
12 | 12 | "\n", |
13 | | - " - [Apache Spark](https://spark.apache.org)\n", |
14 | | - " - [Dask](https://dask.org) (coming soon)\n", |
15 | | - " - [Ray](https://ray.io) (coming soon)\n", |
| 13 | + " - Apache Spark\n", |
| 14 | + " - Desk (coming soon)\n", |
| 15 | + " - Ray (coming soon)\n", |
16 | 16 | "\n", |
17 | 17 | "Let's get started!" |
18 | 18 | ] |
|
26 | 26 | "First, you need a SKE (Star Kubernetes Engine) cluster from [staroid.com](https://staroid.com) and access token for it. SKE provides a fully managed, serverless Kubernetes namespace on the cloud.\n", |
27 | 27 | "\n", |
28 | 28 | " - Sign up [staroid.com](https://staroid.com)\n", |
29 | | - " - Click 'Kubernetes' -> 'New Kubernetes cluster' to create a new SKE cluster. And set `STAROID_SKE` environment variable.\n", |
30 | | - " - Get access token from 'Account' -> ['Access tokens'](https://staroid.com/settings/accesstokens) menu. And set `STAROID_ACCESS_TOKEN` environment variable." |
| 29 | + " - Click 'Kubernetes' -> 'New Kubernetes cluster' to create a new SKE cluster. And set the `STAROID_SKE` environment variable.\n", |
| 30 | + " - Get access token from the 'Account' -> ['Access tokens'](https://staroid.com/settings/accesstokens) menu. And set the `STAROID_ACCESS_TOKEN` environment variable." |
31 | 31 | ] |
32 | 32 | }, |
33 | 33 | { |
|
47 | 47 | "metadata": {}, |
48 | 48 | "source": [ |
49 | 49 | "Now you're ready to go!.\n", |
50 | | - "Let's install and initialize [ods](https://github.com/open-datastudio/ods) module." |
| 50 | + "Let's install and initialize the [ods](https://github.com/open-datastudio/ods) module." |
51 | 51 | ] |
52 | 52 | }, |
53 | 53 | { |
54 | | - "cell_type": "markdown", |
| 54 | + "cell_type": "code", |
| 55 | + "execution_count": null, |
55 | 56 | "metadata": {}, |
| 57 | + "outputs": [], |
56 | 58 | "source": [ |
57 | 59 | "## Install" |
58 | 60 | ] |
|
86 | 88 | "source": [ |
87 | 89 | "## Spark cluster\n", |
88 | 90 | "\n", |
89 | | - "Getting Spark cluster is simple. Create a spark session using ods api. The api will download Spark (3.0.0), configure it, create workers on the cloud and connect to them automatically." |
| 91 | + "Getting a Spark cluster is simple. Create a spark session using ods library. The library will download Spark (3.0.0), configure it, create workers on the cloud, and connect to them automatically." |
90 | 92 | ] |
91 | 93 | }, |
92 | 94 | { |
|
134 | 136 | "source": [ |
135 | 137 | "## Stop Spark session and clean up\n", |
136 | 138 | "\n", |
137 | | - "When spark is no longer needed, you can stop spark session and release executors." |
| 139 | + "When the spark is no longer needed, you can stop the spark session and release executors." |
138 | 140 | ] |
139 | 141 | }, |
140 | 142 | { |
|
165 | 167 | "source": [ |
166 | 168 | "## Commercial support\n", |
167 | 169 | "\n", |
168 | | - "[Staroid](https://staroid.com) actively contributes to Open data studio project and provides a commercial support. Please [contact](https://staroid.com/site/contact)." |
| 170 | + "[Staroid](https://staroid.com) actively contributes to Open data studio and provides commercial support. Please [contact](https://staroid.com/site/contact)." |
169 | 171 | ] |
170 | 172 | }, |
171 | 173 | { |
|
0 commit comments