Heimdall Data 2026: Benefits, Features & Pricing

We implemented Heimdall primarily as a caching layer in front of both a standard PostgreSQL datastore as well as an Apache HAWQ datastore. It was very easy to get set up and get started. It was reasonably well integrated with AWS which made the initial configuration much easier. This was a very easy way for us to get started testing Heimdall on something more than a local database.

It was a pleasure to find Heimdall Data and work with their team on several projects. The Heimdall Data Access Platform was the only solution I found for monitoring and evaluating database traffic that was occurring in volumes of 1GB per five minute period. Through use of this tool, I found a vendors defect and pointed to previously unknown and unquantified performance degradations that were periodic. I would recommend this for any database-dependent application because of this visibility. Heimdall Data through optimizations can also remedy peculiarities of a production application or CoS software package that a vendor may be slow to patch. My initial PoC with Heimdall found and documented within a week, persistent and elusive application issues that I had spent months collecting packets for analysis in Riverbed tools. With the packet captures the volume of data was too large and made it impossible to perform troubleshooting of sporadic events. The aggravation and hassle of packet analysis was quite frustrating, I literally lost whole weekends fighting to import packets with out Riverbed crashing. In contrast, The Heimdall Data install was painless on the App server, requiring only a simple configuration and was collecting data immediately on the wire. There was no modification of the Database server required. A good point to note as many DBAs are quite reluctant to install agents on DB servers as a matter of course. Within the week, in consultation with Heimdall Data the anomalies and performance degradations were pinpointed. It was a pleasure working with the whole team and their insight and ability to provide assistance pushed the PoC through to a documented success. I would recommend Heimdall Data to anyone troubleshooting sporadic and performance issues - the icing on the cake is that Heimdall Data can optimize the database traffic as well, and this is a serious issue to consider when dealing with closed source or complex schema. I believe that where Heimdall Data shines is the support they perform in a PoC the expertise they bring to the table is phenomenal and this is why I must give the tool four stars. I think the customer service and rapid turn around that I saw in the PoC demonstrated significant time saved. While you may be able to get up to speed in a year and be prepared to deploy future applications on your own, I would ask for a Professional Services quote first as they are quite efficient and can save you time.

The problem we are trying to solve is providing means for intelligent automatic failover for database server clusters. At the same time it is great to have the performance enhancing features such as intelligent read/write splits caching. Support has been oustanding. Heimdall data, like the solutions before it was not 100% trouble free to setup. The difference with Heimdall though was the quality and responsiveness of the support. I got support turnarounds in hours or even less with at least info on what the problem is if not communication that they understood the issue and will have a fix for it soon. Compare to a (worst case) scenario with a previous vendor where it literally took more than two years to get resolution on one SQL distribution problem.

Performance plus insight into how to get even more performance. This has allowed us to better satisfy our app customers while cutting the cost of resources.

We moved a small genomics Java based website to AWS and wanted to implement SQL caching and Read/Write splitting for the database that we had migrated to Aurora MySQL Compatible. Heimdall Data allowed us to do both with no code changes in an afternoon and our overall page loading performance improved 93% over a 2 week testing period. Support reached out during our trial and provided very useful guidance on how best to implement a solution that would work for us. Great software and great support that has allowed us to improve performance and focus on developing new features not changes to support caching and read/write splitting.

Heimdall gave us instant insight into DB issues that had plagued us for months. Armed with data we were able to work with engineering teams to resolve the issues. With the database issues behind us we were able to start using the caching and traffic routing functions which reduced load on databases tremendously. Heimdall saved on the cost of larger databases many times over.

In a shot time, your installation will be complete.

I was prototyping Heimdall Data's solution with a local application. Primarily to learn it, with the plan of implementing in a production cloud application in the near future. First and foremost, I'm impressed with the ease of installation. I had it up and running my application (python) and a MySQL database. It probably took all of 10 minutes. Once it was up and running, and I found how to configure the 'analyze' component, I literally had a one click cache option. Ok, maybe it was 2-3 clicks to setup, enable and then monitor the speed improvements of my application. The built-in charts/graphs show cache hits, query performance etc. Very slick to see the improvements. I know there are other features, that I just haven't gotten to yet. I'm very interested in the redundant/failover use case. I'll be doing a blog/write up @ http://fauie.com in the super near future about my findings.