Autonomously Optimize Hadoop and Spark clusters on Azure HDInsight

Continuous and autonomous optimization for Apache Hadoop empowering more efficient data engineering, data science, and machine learning

One optimization solution for all HDInsight use cases

Achieve faster, more efficient big data applications with Intel Tiber App-Level Optimization

Batch/streaming data

Data Science at Scale

SQL Analytics

Machine Learning

Integrates with all major data storage and infrastructure

Complete more HDInsight jobs in less time

Intel Tiber App-Level Optimization allows data science, data engineering and data analysis teams to improve big data application performance autonomously and reduce cluster size

YARN resource allocation

Optimized to improve cluster density and remove overprovisioning waste.

Spark dynamic allocation

Optimized dynamic allocation and removal of executors based on the job patterns and predictive idle heuristics.

JVM runtime optimization

JNI overhead reduction, execution control flow and reflection overhead optimization

Memory arenas optimization

Release of memory space and object sizes to reduce allocation overhead

Crypto & compression acceleration

Leveraging Crypto architecture, accelerators, and instruction sets for operations

“We saw the effect on the costs right away. After implementing Intel Tiber App-Level Optimization we saw a 50% memory reduction and a 20% CPU reduction, which eventually translated to an 18% cost reduction… Looking forward, we’re going to install Intel Tiber App-Level Optimization on all the other apps that we have”

Uri Harduf

DevOps Group Manager

“Intel Tiber App-Level Optimization went above and beyond, achieving 58% average reduction in response time. It never occurred to us that we might improve performance so much that reducing cost was an option. But we were able to leverage these results into a cost reduction of 35%!”