Working in the notebook environment

Important

Amazon FinSpace Dataset Browser will be discontinued on November 29, 2024. Starting November 29, 2023, FinSpace will no longer accept the creation of new Dataset Browser environments. Customers using Amazon FinSpace with Managed Kdb Insights will not be affected. For more information, review the FAQ or contact AWS Support to assist with your transition.

Choosing Go to Notebook or Analyze in Notebook will open Jupyter Lab in a new tab in your web browser. You will land in the launcher page of SageMaker studio.

To start a notebook with FinSpace kernel

1. In the upper-left corner of SageMaker Studio, choose Amazon SageMaker Studio to open Studio Launcher.

2. On the Launcher page, choose Notebooks and compute resources.

3. For Select a SageMaker image, choose the FinSpace PySpark image.

4. Choose Notebook to create a notebook in the FinSpace PySpark image.

FinSpace kernel

The FinSpace PySpark Kernel comes with all the libraries required to access and work with data stored in FinSpace, including the Spark Cluster management API and time series analytics library. The FinSpace Cluster Management API is used to instantiate and connect the notebook instance to a dedicated Spark Cluster. FinSpace Spark clusters use Kerberos authentication for additional security. FinSpace provides with complete resource isolation when working with Spark Clusters.

When a FinSpace PySpark Kernel is instantiated for the first time in a new notebook session, you can expect a startup time of about 3 to 5 minutes to allow bootstrapping of all dependencies on the image supporting the notebook.