The Heimdall Data is a software platform for application developers, database administrators, and infrastructure owners. Whether on-premise or cloud, Heimdall helps organizations deliver faster, more reliable, and secure content generation.
Heimdall Data is a distributed Database Proxy that optimizes from an application perspective. It gives users visibility and control over their SQL environment. Installed on each application instance, there is no single point of failure and each Heimdall instance acts independently, yet in unison for caching, read/write splitting, and automated database failover.
Caching: Today’s SQL solutions incur data latency from application-database round trips. Caching on the database improves scale, but does not remove the latency. Heimdall Data is sidecar Database Proxy installed across application instances. This distributed model results in optimal performance and predictive scale.
With Heimdall caching, queries that would have gone to the database are now cached locally on the application tier. You choose the cache of choice (e.g. Amazon Elasticache, Redis) and Heimdall determines which query results to cache and invalidate. Best of all, caching does not require writing a single line of code.
Read/Write Split: To scale the database tier horizontally with master and read replicas, applications modifications are required. Heimdall is SQL aware and routes queries to the appropriate database instances (writes or read replicas).
Automated Failover: Health checks each database instance. Upon a failure, Heimdall holds the connection as it transitions to the redundant database instance. This results in failovers that are transparent to the application.
The above chart shows how Heimdall Data compares with other vendors (ScaleArc, Oracle, ProxSQL, MaxScale etc.). How are we unique?
- Auto-cache and Auto-invalidation requiring no application changes
- Distributed architecture, scales linearly with the application
- Database vendor neutral, allowing Enterprises to change data sources if required