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docs(dlb): doc update MTA-5604
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pages/data-lab/quickstart.mdx

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- Notebook: A JupyterLab service operating on a dedicated node type.
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Scaleway provides dedicated node types for both the notebook and the cluster. The cluster nodes are high-end machines built for intensive computations, featuring numerous CPUs and substantial RAM.
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Scaleway provides dedicated node types for both the notebook and the cluster. The cluster nodes are high-end machines built for intensive computations, featuring powerful CPUs/GPUs, and substantial RAM.
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The notebook, although capable of performing some local computations, primarily serves as a web interface for interacting with the Apache Spark cluster.
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3. Complete the following steps in the wizard:
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- Choose an Apache Spark version from the drop-down menu.
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- Select a worker node configuration.
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- Select a worker node configuration. For this procedure, we recommend selecting a CPU rather than a GPU.
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- Enter the desired number of worker nodes.
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<Message type="note">
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Provisioning zero worker nodes lets you retain and access you cluster and notebook configurations, but will not allow you to run calculations.
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</Message>
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- Optionally, choose an Object Storage bucket as your source of data and the place to store the output of your operations.
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- Enter a name for your Data Lab.
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- Optionally, add a description and/or tags for your Data Lab.
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- Verify the estimated cost.
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## How to run the demo file
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Each Distributed Data Lab comes with a default `quickstart.ipynb` demo file for testing purposes. This file contains a preconfigured notebook environment that requires no modification to run.
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Each Distributed Data Lab comes with a default `DatalabDemo.ipynb` demonstration file for testing purposes. This file contains a preconfigured notebook environment that requires no modification to run.
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Execute the cells in order to perform pre-determined operations on a dummy data set.
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"name": "My Spark",
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"conf":{
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"spark.hadoop.fs.s3a.access.key": "your-api-access-key",
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"spark.hadoop.fs.s3a.secret.key": "your-api-access-key",
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"spark.hadoop.fs.s3a.secret.key": "your-api-secret-key",
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"spark.hadoop.fs.s3a.endpoint": "your-bucket-endpoint"
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}
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}

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