What is Amazon FinSpace?
Amazon FinSpace is a fully managed data management and analytics service that makes it easy to store, catalog, and prepare financial industry data at scale, reducing the time it takes for Financial Services Industry (FSI) customers to find and access all types of financial data 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-parties, 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 can collect data in a secure data management application and catalog it by relevant business concepts such as asset class, risk classification, or geographic region, which makes it easy to discover and share across your organization. FinSpace includes a library of 100+ functions, like time bars and Bollinger bands, to prepare data for analysis. You can also integrate functions from your own libraries and notebooks for your own analysis. FinSpace supports your organization's compliance requirements by enforcing data access controls and keeping audit logs.
To see all the regions FinSpace is available in, visit the AWS Region page
Managing data in financial services
Financial services customers store petabytes of data which is collected from both internal and external data sources. The data is generated from their internal applications such as portfolio management systems, actuarial applications, order and risk management systems, and external data such as stock exchanges and financial data providers. The data is typically used for use cases including but not limited to quantitative research, product pricing, customer experience, and investment management. The size of the data is growing and the number of sources that FSIs are receiving data from is also increasing which makes it hard to manage and track. FSI customers want to make this data available in a self-service and secure way to their analysts and data scientists for analysis. The analysts want to discover the data easily, and analyze it at scale.
Benefits of Amazon FinSpace
With Amazon FinSpace, you can:
-
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. For more information, see Loading and analyzing data.
-
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. For more information, see Configuring a business data catalog.
-
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.
-
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. For more information, see Prepare and analyze data.
-
Share data managed in FinSpace – Share data view tables with a Lake Formation data lake so that the data can be easily queried with AWS analytics engines like Amazon Redshift, Athena, Amazon QuickSight,Amazon EMR, and SageMaker. For more information, see Data views sharing.
-
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. For more information, see Time series library.
How Amazon FinSpace works
To use FinSpace
Launch FinSpace from your Amazon Web Services (AWS) console, and configure how data will be organized in the catalog for easy searching.
Add data that will be needed for analytics.
Organize and describe the data so that it can be searched from the catalog.
Prepare data by creating historical or current data views partitioned to optimize performance.
Analyze data using integrated Jupyter notebooks and managed Spark clusters for data processing at scale.
