Easy to manage high-performance time-series database and analytics for Capital Markets

Why FinSpace?

Amazon FinSpace simplifies running kdb Insights applications on AWS. Amazon FinSpace automates the undifferentiated tasks required to provision, integrate, and secure infrastructure for kdb Insights. In addition, Amazon FinSpace provides easy to use APIs so customers can configure and run new kdb Insights applications in just a few minutes. Amazon FinSpace gives customers the flexibility required to move existing kdb Insights applications to AWS and get the benefits of cloud, while eliminating the complex and costly work of self-managing the infrastructure. KX's kdb Insights is a high-performance analytics engine that is optimized for analysis of real-time and multi-petabyte historical time-series data. Kdb Insights is commonly used by Capital Markets customers to power business-critical workloads, such as options pricing, transaction cost analysis, and backtesting.

Benefits

Deploy new kdb Insights applications in minutes

Eliminate the work to integrate more than 15 AWS services to deploy kdb. Easy to use APIs enable you to provision kdb infrastructure, with kdb Insights, in minutes to support backtesting, ad-hoc analytics, and quant research.

Accelerate migrating kdb Insights applications to AWS

Managed infrastructure, easy to use APIs, and flexible support for broad range of kdb architectures reduces the time to migrate kdb application to AWS.

Reduce spend on kdb Insights infrastructure

Lower total cost of ownership with a fully managed infrastructure, flexibility to easily scale up and down as needed, and tiered database storage and query caching.

Seamlessly integrates kdb Insights with AWS

Use Amazon CloudTrail to track all API calls, Amazon CloudWatch to monitor and alert on performance , and AWS Identity and Access Management (IAM) to secure access to your kdb databases.

Features

Secure and scalable data management for kdb

Easily store kdb database files, encrypted with a KMS key you provide. Use of cloud object store technology seamlessly scales the kdb database as data volumes grow. Integrated, and configurable, high performance caching accelerates important queries, while managing the cost of what data is cached. Built-in database versioning allows you to travel back in time to query your data as it existed at a previous point in time.

Launch managed kdb clusters integrated with AWS services

Using simple APIs, customers can launch and configure kdb clusters. Managed kdb Insights eliminates much of the operational overhead and provides pay as you go compute for kdb Insights applications. Managed kdb Insights is integrated with Terraform, to enable integration with CI/CD workflows, and AWS services, such as CloudWatch, CloudTrail, and IAM Permissions.

Autoscaling and High Availability to keep up with business demand

Managed kdb Insights includes configurable autoscaling to ensure that kdb applications can keep up with the most volatile market conditions. You can also configure Multi-AZ to ensure kdb applications are highly available during the more important business hours.

Supports existing kdb configurations to simplify and accelerate migration

Configure kdb ticker plants, real time clusters, historical clusters, and gateways to match existing environments to easily migrate existing kdb workloads and Q code. In addition to configuration as code through programmatic AWS APIs, customers can set up, access and manage the kdb environment (database and clusters) via the familiar interface of the FinSpace console.

Use cases

Setup centralized tick database

Provide access across your organization to a centralized database with years of historical market data. Eliminate data copies, ensure high quality data, and provide high-performance analytics across billions of rows of data.

Burst kdb applications to AWS for elasticity and agility

Gain benefits of cloud scale to address on-premises infrastructure constraints and keep up with fluctuating market conditions.

Ad hoc high-perf analytics environments for market data

Reduce time to backtest trading algorithms and support quant research. Leverage on-demand compute to validate trading strategies against petabytes of historical market data (i.e. backtesting).

Perform pre/post trade analytics on real-time and historical data

Easily setup secure and scalable environments to perform critical trading analytics for capital markets. Ingest and process large sets of market and transactional data through a high-performance time-series database, and auto-scale compute to keep up with demand.