Amazon FinSpace

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Amazon FinSpace is a data management and analytics service purpose-built for the financial services industry (FSI). FinSpace reduces the time you spend finding and preparing petabytes of financial data to be ready for analysis from months to minutes.

Financial services organizations analyze data from internal data stores like portfolio, actuarial, and risk management systems as well as petabytes of data from third-party data feeds, such as historical securities prices from stock exchanges. It can take months to find the right data, get permissions to access the data in a compliant way, and prepare it for analysis.

FinSpace removes the heavy lifting of building and maintaining a data management system for financial analytics. With FinSpace, you collect data and catalog it by relevant business concepts such as asset class, risk classification, or geographic region. FinSpace makes it easy to discover and share data across your organization in accordance with your compliance requirements. You define your data access policies in one place and FinSpace enforces them while keeping audit logs to allow for compliance and activity reporting. FinSpace also includes a library of 100+ functions, like time bars and Bollinger bands, for you to prepare data for analysis.

Key Features

To use FinSpace:

  1. Launch FinSpace from your AWS console, and configure how data will be organized in the catalog for easy searching.

  2. Add data that will be needed for analytics.

  3. Organize and describe the data so that it can be searched from the catalog.

  4. Prepare data by creating historical or current data views partitioned to optimize performance.

  5. Analyze data using integrated Jupyter notebooks and managed Spark clusters for data processing at scale.

Key Benefits

With Amazon FinSpace, you can:

  1. Import data easily — The SDKs allows you to load data files into FinSpace in bulk, daily, or ad-hoc fashion. Connect your daily historical data feeds from stock exchanges and data providers into FinSpace.

  2. Store and catalog data with business terms — Create a business data catalog with your business taxonomy to organize data so that your business users can easily discover it. Organize data by asset classes, regions, data types, or industry.

  3. Track versions of data — Create bi-temporal views that let you analyze data the way it looked at a particular date and time. Reproduce historical financial models for audit and compliance purposes.

  4. Prepare and analyze data at scale — Use FinSpace notebook with integrated managed Spark clusters to run analysis on petabytes of data. Scale compute with spark clusters on an as-needed basis

  5. Financial time series analysis — Run financial time series analysis on high density market data using integrated time series library with over 100 embedded functions including statistical and technical indicators such as Bollinger Bands.