Dataset Browser

Amazon FinSpace Dataset Browser will reach end of life on November 29, 2024. Customers using the Amazon FinSpace with Managed kdb Insights will not be affected. Starting November 29 2023, the service will no longer accept the creation of new Dataset Browser environments. Existing Dataset Browser customers can now use Amazon DataZone for their business data catalog needs, Amazon Athena for Apache Spark for their data processing, and Amazon SageMaker Studio for their notebooks. If you are currently using Dataset Browser and need help with migration, contact AWS Support for assistance with migration options. For other question related to Dataset Browser end of life, you can review the FAQ or reach out to AWS Support to assist with your transition.

Flexible data import and secure storage: FinSpace’s APIs allow you to ingest data from your internal and third-party data feeds, such as risk management systems or historical securities prices from stock exchanges, and existing S3 buckets. You can also drag and drop files using the web application. Your data is then encrypted in FinSpace using an AWS Key Management Service (KMS) key that you create and manage.

Business Data Catalog: Data loaded into FinSpace is automatically tracked in the business data catalog. FinSpace's business data catalog makes it easy for you to find, learn about, and access data directly instead of relying on a technical team to provision data for analysis.

Metadata and classifications: In FinSpace, you can use metadata to provide business context and meaning to your data so that it is easier to organize and understand. In order to describe your data in more structured ways, like by provider, frequency of delivery, licensing terms, and more, you can add classifications and associate attributes by writing in text or selecting classifications from pre-made drop downs. With FinSpace, you can also define classifications based on your business terms and usage and set rules to ensure that all metadata is captured in the same way.

Automatic application of corrections to time series data: FinSpace is designed to simplify the collecting and processing of data common in the financial services industry, such as time series data and reference data. As data is periodically collected, FinSpace can apply corrections or revisions to your time series data so that no additional post-processing is required in order for you to perform analysis. FinSpace also automatically converts all collected raw data into compute optimized formats like Apache Parquet that are more conducive to analysis.

Financial time series data processing library: FinSpace’s financial time series data processing library allows you to transform and enrich time series data. The library of 100+ functions can be used to run financial analytics in your Jupyter notebooks, such as the computation of statistical and technical indicators to support investment and risk management decisions. It also provides functions to filter and normalize time series data such as a stock's open, high, low, and close prices into time bars.

Historical data views: FinSpace makes it easy to validate modeling assumptions using historical data by enabling you to explore and use datasets at any point in time, such as when the data was originally collected.

Integrated Jupyter notebooks: When you want to perform data preparation and run analysis in FinSpace, you can access all data directly from Jupyter notebooks built-in to integrate with your data. FinSpace allows you to securely share data created in your notebook with others on your team via the FinSpace catalog.

Data access controls and audit reports: FinSpace lets you define access policies in one place and enforces them across data search, visualization, and analysis. FinSpace also records access and operations that you and your systems perform on data. You can use FinSpace audit reports to demonstrate compliance with your data governance policies.

Fully managed service: FinSpace eliminates the need to integrate a financial data management system with analysis tools and build all the components required to do so, such as bi-temporal storage, a data catalog, Spark clusters, and more. Your teams only need to integrate FinSpace with your data sources to get started.

Managed Spark compute: With FinSpace, you can launch managed Spark clusters from the Jupyter notebook to easily process data at scale, perform data transformations, and run analytics. You can select from one of the pre-configured cluster templates to match the size and complexity of the operation you need to perform.