Boost Greenplum BI Performance with Heimdall Data

Eric Brandsberg, CTO of Heimdall Data and Greg Chase, Business Development Lead at Pivotal, chat with Jeff Kelly about how Heimdall Data can help you boost Greenplum BI Performance

One of the more interesting startups I’ve run across recently is Heimdall Data. They are a “SQL-savvy” platform that elegantly slips into your application instance for improved data performance and reliability of backend data sources.

Here are some use cases for Pivotal Greenplum that we have been developing with Heimdall:

• SQL Caching — This will be super helpful for a Pivotal Greenplum instance when it supports traditional BI users who make repeated queries. Heimdall Data auto-caches and auto-invalidates SQL results into Pivotal GemFire without code changes. Users will experience faster response times while freeing up the system workloads for what your data scientists might run.
• Automated Master Failover — Typical Pivotal Greenplum deployments have a standby master node. However, actually failing over to the standby in the event of a failure of the active master node is a manual process. Heimdall can automate failover to the standby master node Greenplum. Heimdall is an enhancement over Pgpool as it handles failover behind the scenes without the need for development work.
• SQL Traffic Manager — We also think Heimdall could be useful for customers that want to combine OLTP with their analytics, such as an HTAP application. In this case Heimdall determines whether SQL operations it received should be executed by Pivotal Greenplum, an associated PostgreSQL database, or even processed by both. This provides the kind of performance that transactional applications expect, and also means the Pivotal Greenplum data warehouse has access to up to date data. Like most of Heimdall’s approaches, this solution also slips in, and requires no code changes to your SQL queries to work.

Listen to our interview with Heimdall CTO Eric Brandsberg from Greenplum Summit 2018 in Jersey City this last May.

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