After careful consideration, we decided to end support for Amazon FinSpace, effective October 7, 2026. Amazon FinSpace will no longer accept new customers beginning October 7, 2025. As an existing customer with an Amazon FinSpace environment created before October 7, 2025, you can continue to use the service as normal. After October 7, 2026, you will no longer be able to use Amazon FinSpace. For more information, see [Amazon FinSpace end of support](https://docs.aws.amazon.com/finspace/latest/userguide/amazon-finspace-end-of-support.html).
# Working with Amazon FinSpace notebooks
**Important**
Amazon FinSpace Dataset Browser will be discontinued on *March 26, 2025*. Starting *November 29, 2023*, FinSpace will no longer accept the creation of new Dataset Browser environments. Customers using [Amazon FinSpace with Managed Kdb Insights](https://aws.amazon.com/finspace/features/managed-kdb-insights/) will not be affected. For more information, review the [FAQ](https://aws.amazon.com/finspace/faqs/) or contact [AWS Support](https://aws.amazon.com/contact-us/) to assist with your transition.
Amazon FinSpace notebook provides an Integrated Development Environment (IDE) that lets you access data from the FinSpace Data Catalog to perform data preparation and analysis. FinSpace simplifies the use of Apache Spark providing access to fully managed Spark Clusters using easy to launch cluster templates. For more information, see [Apache Spark](https://spark.apache.org/).
**Note**
In order to use notebooks and Spark clusters, you must be a superuser or a member of a group with necessary permissions - **Access Notebooks, Manage Clusters**.
The Spark clusters are terminated daily at midnight US Eastern time.
FinSpace notebooks are programmed using Python. Python and Spark integration is achieved using the PySpark library. For more information, see [PySpark](https://spark.apache.org/docs/latest/api/python/).
** **Topics** **
+ [
# Opening the notebook environment
](opening-the-notebook-environment.md)
+ [
# Working in the notebook environment
](working-in-the-notebook-environment.md)
+ [
# Access datasets from a notebook
](access-datasets-notebook.md)
+ [
# Example notebooks
](example-notebook.md)
# Opening the notebook environment
**Important**
Amazon FinSpace Dataset Browser will be discontinued on *March 26, 2025*. Starting *November 29, 2023*, FinSpace will no longer accept the creation of new Dataset Browser environments. Customers using [Amazon FinSpace with Managed Kdb Insights](https://aws.amazon.com/finspace/features/managed-kdb-insights/) will not be affected. For more information, review the [FAQ](https://aws.amazon.com/finspace/faqs/) or contact [AWS Support](https://aws.amazon.com/contact-us/) to assist with your transition.
**Note**
In order to open a notebook environment, you must be a superuser or a member of a group with necessary permissions - **Access Notebooks**.
Expect a one-time setup delay of 15-20 minutes for the notebook environment after creating a new user.
**You can open a notebook environment in the following ways**
+ Using the data view cards on homepage.
+ From the dataset details page under **Data Overview** tab.
+ From the dataset details page under **All Data Views** tab.
## Access notebook from homepage
**To access notebook environment from the recently created data views**
1. Sign in to the FinSpace web application. For more information, see [Signing in to the Amazon FinSpace web application](signing-into-amazon-finspace.md).
1. From the homepage, under the **Status of Data Views** section, find the recently created data view.
1. On the data view card, choose **Analyze**. The notebook opens in a new tab on your browser.
## Access notebook from Data Overview tab
**To access notebook environment from the overview tab**
1. Sign in to the FinSpace web application. For more information, see [Signing in to the Amazon FinSpace web application](signing-into-amazon-finspace.md).
1. From the homepage, search for a dataset.
1. Choose the dataset name to view the dataset details page.
1. From the **Data Overview** tab, under **Analyze Data** section, choose **Analyze in Notebook** in the data view card.
The notebook opens in a new tab on your browser.
![\[A screenshot that shows the data overview tab.\]](http://docs.aws.amazon.com/finspace/latest/userguide/images/07-prepare-and-analyze-data/data-view-card.png)
****
## Access notebooks from All Data Views tab
**To access notebook environment from the list of all data views for a dataset**
1. Sign in to the FinSpace web application. For more information, see [Signing in to the Amazon FinSpace web application](signing-into-amazon-finspace.md).
1. From the homepage, search for a dataset.
1. Choose the dataset name to view the dataset details page.
1. Choose **All Data Views** tab.
1. From the **Data Views** table, choose **Analyze in Notebook** for any of the data views.
The notebook opens in a new tab on your browser.
![\[A screenshot that shows the All Data Views tab.\]](http://docs.aws.amazon.com/finspace/latest/userguide/images/07-prepare-and-analyze-data/all-data-views-tab.png)
# Working in the notebook environment
**Important**
Amazon FinSpace Dataset Browser will be discontinued on *March 26, 2025*. Starting *November 29, 2023*, FinSpace will no longer accept the creation of new Dataset Browser environments. Customers using [Amazon FinSpace with Managed Kdb Insights](https://aws.amazon.com/finspace/features/managed-kdb-insights/) will not be affected. For more information, review the [FAQ](https://aws.amazon.com/finspace/faqs/) or contact [AWS Support](https://aws.amazon.com/contact-us/) 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.
1. On the **Launcher** page, choose **Notebooks and compute resources**.
1. For **Select a SageMaker image**, choose the FinSpace PySpark image.
1. 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.
# Access datasets from a notebook
**Important**
Amazon FinSpace Dataset Browser will be discontinued on *March 26, 2025*. Starting *November 29, 2023*, FinSpace will no longer accept the creation of new Dataset Browser environments. Customers using [Amazon FinSpace with Managed Kdb Insights](https://aws.amazon.com/finspace/features/managed-kdb-insights/) will not be affected. For more information, review the [FAQ](https://aws.amazon.com/finspace/faqs/) or contact [AWS Support](https://aws.amazon.com/contact-us/) to assist with your transition.
You can conveniently and securely access all datasets to prepare and analyze data from your Amazon FinSpace notebook. The following sections show how to access data from a FinSpace notebook.
**Note**
In order to use notebooks and Spark clusters, you must be a superuser or a member of a group with necessary permissions - **Access Notebooks, Manage Clusters**.
## Access data using a pre-populated notebook
**To access data using a pre-populated notebook**
1. Sign in to the FinSpace web application. For more information, see [Signing in to the Amazon FinSpace web application](signing-into-amazon-finspace.md).
1. Open a notebook by using one of the three methods listed in [Opening the notebook environment](opening-the-notebook-environment.md).
In the notebook, the dataset ID and data view ID are pre-populated.
1. Run all cells to print the schema and content of the data view.
## Access data using a newly created notebook
**To access data using a newly created notebook**
1. Run the following code from your notebook to instantiate a cluster and connect the FinSpace PySpark image to the cluster.
```
%local
from aws.finspace.cluster import FinSpaceClusterManager
finspace_clusters = FinSpaceClusterManager()
finspace_clusters.auto_connect()
```
The output should be similar to the following output
```
Cluster is starting. It will be operational in approximately 5 to 8 minutes
Started cluster with cluster ID: 8x6zd9cq and state: STARTING
......
cleared existing credential location
Persisted krb5.conf secret to /etc/krb5.conf
re-establishing connection...
Persisted keytab secret to /home/sagemaker-user/livy.keytab
Authenticated to Spark cluster
Persisted Sparkmagic config to /home/sagemaker-user/.Sparkmagic/config.json
Started Spark cluster with clusterId: 8x6zd9cq
finished reloading all magics & configurations
Persisted FinSpace cluster connection info to /home/sagemaker-user/.Sparkmagic/FinSpace_connection_info.json
SageMaker Studio Environment is now connected to your FinSpace Cluster: 8x6zd9cq at GMT: 2021-01-15 02:13:50.
```
**Note**
Without the `%local` at the beginning of the cell, your code will be executed on the Spark cluster.
1. To access the data view, you will need the dataset ID and data view ID. To get these IDs
1. In the FinSpace web application, open the dataset details page of the dataset that you want to analyze.
1. Under the **All Data Views** tab, find the data view that you want to analyze.
1. Choose **Details**.
1. Copy the **Data View ID** and **Dataset ID** to use in the notebook.
1. Initialize dataset ID and data view ID in the notebook.
```
dataset_id = "rgg1hj1"
data_view_id = "VrvKEKnA1El2nr821BaLTQ"
```
1. Instantiate FinSpace Analytics Manager to access the data and read into a Spark DataFrame.
```
from aws.finspace.analytics import FinSpaceAnalyticsManager
finspace_analytics = FinSpaceAnalyticsManager(Spark = Spark)
df = finspace_analytics.read_data_view(dataset_id = dataset_id, data_view_id = data_view_id)
```
# Example notebooks
**Important**
Amazon FinSpace Dataset Browser will be discontinued on *March 26, 2025*. Starting *November 29, 2023*, FinSpace will no longer accept the creation of new Dataset Browser environments. Customers using [Amazon FinSpace with Managed Kdb Insights](https://aws.amazon.com/finspace/features/managed-kdb-insights/) will not be affected. For more information, review the [FAQ](https://aws.amazon.com/finspace/faqs/) or contact [AWS Support](https://aws.amazon.com/contact-us/) to assist with your transition.
You can access example notebooks and Python scripts illustrating how to use Amazon FinSpace to prepare and analyze data using Spark Clusters and the time series analytics library. You can clone the gitrepo in your Jupyter Lab for easy access to the example notebooks.