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Heimdall Proxy Standard Edition

Heimdall Data |

Linux/Unix, Amazon Linux 2 - 64-bit Amazon Machine Image (AMI)

Reviews from AWS Marketplace

15 AWS reviews


Heimdall Data --> a hidden gem!

  • June 01, 2019
  • Review verified by AWS Marketplace

We learned about Heimdall Data while looking for an easy-to-integrate caching solution for our Java application. We were having some growing pains with lots of new customers and we weren't ready for the higher volumes. We knew we had to do something - fast! To our surprise, implementing Heimdall was easy. We didn't have to do any code changes to our application to take advantage of their technology.

We deployed Heimdall Data to our production environment on AWS and soon starting seeing ~80% cache hit rates! We immediately noticed an improvement in response times and the load on the database was substantially lower. We have been running Heimdall Data in production for over 6 months and have had great results helping us scale without having to hire a DBA or increase AWS hardware specs ($$).

Other positives - Heimdall has a very good reporting system which provides monitoring and analytics of database queries. This helps identify any issues and optimize our database queries. In addition Heimdall provides very good support. They have very good expertise on AWS and some of their advice helped improve performance of our product.

I highly recommend them!

    Craig S. Connell

Easy to get started with good performance

  • December 19, 2018
  • Review verified by AWS Marketplace

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.

    Randy Ehli, CEO

Heimdall Data Delivers Real Performance

  • February 15, 2018
  • Review verified by AWS Marketplace installed the Heimdall Data Distributed Caching system to it AWS system. We use AWS application load balancer for our EC2 Instances, 3 Aurora MySql databases of which 2 are read only with auto scaling. Heimdall Data's reporting system provided insights in how we could optimize our SQL scripts. This alone made the product very useful. Heimdall Data utilizes Elastic Cache for Redis as part of their caching grid. The best part was we didn't have to do any code changes to our application to take advantage of this caching system. We just turn on the Auto Tune feature and let it work. We are an online auction company and this caching system has improved our web site response rate during a load on average 27 percent on up to 46 percent. They support their product and are there to help you take full advantage of their caching solution.


Great Product

  • February 15, 2018
  • Review verified by AWS Marketplace

We were very glad to find Heimdall Data and eventually implement their product. Their platform handles the caching layer for database queries, among many other things. The only comparable solution we came across in the market is Pgpool, but Heimdall Data has proven to be in a class of its own.

Pgpool handles connection pooling, read/write splitting, and manual caching, However, it is all manual and requires code changes. Pgpool also does not support SELECT FOR UPDATE and several other options we found readily available through Heimdall Data.

We first tested Pgpool and very quickly realized that it would require extensive work to update, maintain and scale. When we began testing with Heimdall Data we were able to see huge improvements in load testing very quickly due to its automation. We were able to increase our test throughput about 5-7x what original performance tests had shown. We continued to test and learned all about the rules engine, explain plans, and analytics which allowed us to quickly identify several places for optimization. We could easily see query response sizes, response times, and query count, among others.

Heimdall data has allowed us to see into the database queries and transaction times on both the application and Amazon Aurora. The level of monitoring and analytics that Heimdall Data provides allowed us to find and identify issues that only a seasoned DBA would be able to gather previously.

We deployed Heimdall Data to our production environment and have been seeing between 60-70% cache hit rate overall. Immediately we noticed an improvement in response times and the benefit of our application and database load being reduced greatly. We have been running Heimdall Data in production for over 2 months and have had great results. This has been an invaluable tool for helping us scale and allowing us to keep resources low.

Overall, We have been very satisfied with Heimdall Data and can highly recommend it.

    Ka Lun Chan

Heimdall Data auto-caching came to our rescue

  • January 18, 2018
  • Review verified by AWS Marketplace

Performance is a major concern for any application developer and organization. Often enough, people don't worry about it until revenue, organic traffic and session time start to dip or when they see high infrastructure costs on P&L. This needs to be something everyone focuses on up front, not when the ceiling is falling down. When performance needs to be improved, caching is often the first step taken. The best caching is the type used without involving your application.

Heimdall Data auto-caching came to our rescue. We were able to develop our application 10x faster without additional coding and worry about what/when to cache/expire to improve our time to market. The analytic tool is awesome! It helped us find performance bottlenecks very quickly. We were able to see SEO ranking, user session times, search engine page crawled per day improve day after day while saving for development and infrastructure cost. We spent less time finding bottlenecks, coding, and reduced cost on databases. RDS is our most expensive item in our infrastructure and we were able to reduce our databases cost by using Hemidall Data analytic tools to find/fix the bottleneck very quickly and caching more.