Amazon FinSpace Documentation

FinSpace’s APIs are designed to 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.

Data loaded into FinSpace is tracked in the business data catalog. FinSpace's business data catalog enables you to find, learn about, and access data directly.

In FinSpace, you can use metadata to help 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 premade 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.

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 is designed to 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 is also designed to convert all collected raw data into compute optimized formats like Apache Parquet that are more conducive to analysis.

FinSpace’s financial time series data processing library is designed to allow you to transform and enrich time series data. The library of 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.

FinSpace helps you 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.

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 is designed to allow you to share data created in your notebook with others on your team via the FinSpace catalog.

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. FinSpace audit reports are designed to help you to demonstrate compliance with your data governance policies.

FinSpace is designed to eliminate 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 catalag, Spark clusters, and more. Your teams only need to integrate FinSpace with your data sources to get started.

With FinSpace, you can launch managed Spark clusters from the Jupyter notebook to 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.

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.aws.amazon.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.amazon.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.