Handle more HDFS metadata and larger clusters while reducing infrastructure costs and complexity.

Increase NameNode capacity, reduce pause times and failover.

Azul customers running Hadoop and HBase big data technologies drive infrastructure cost savings and performance improvements across data lake, data engineering, and big data analytics applications.

Reduce stop-the-world pauses.

We love when our customers tell us they reduced worst-case pauses from 34 seconds down to 35 milliseconds, facilitating larger heaps and allowing larger DataNode clusters.

Reduce Hadoop NameNode start-up time.

We also love it when our customers tell us that with Azul they reduced NameNode startup time from 10 hours to 30 minutes.

Improve stability and performance of Hadoop NameNodes.

And we love it most when customers tell us, as many have, that they improved throughput by 25-40% and enabled 3x-6x more objects on NameNodes than on OpenJDK.

“The Azul Platform performance is so superior that we’re now doing things with our big data system that cannot be done without Azul.”

Ariel Pisetzky, VP of IT, Taboola

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Hadoop at Taboola. Powered by Azul.

“This translates into millions of dollars in savings, both hardware we freed up and hosting costs avoided. The ROI of the project is just self-evident.”

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Explore big data technologies with Azul.

Whatever your big data stack, where it’s based on Java and the JVM, Azul Platform Prime reduces infrastructure costs and improves performance, as well as Hadoop and HBase but also Kafka, Cassandra, Spark and many more.

Explore Big Data with Azul

224% ROI and payback in under 3 months for Azul Platform Prime.

Azul commissioned Forrester Consulting to conduct a Total Economic Impact™ study to evaluate Azul Platform Prime’s return on investment based on customer results.

Read the Study

Hadoop and Big Data made easy by Azul Platform Prime.

Read the Big Data Solution Brief, featuring Hadoop, Cassandra and Kafka.

Read the Report

Azul powers the modern cloud enterprise.

Azul delivers the turbocharged performance you need to handle the scale of Java-based big data while actually reducing your infrastructure requirements.

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