Optimize Dataproc at Scale for Lower TCO of Data Applications

Continuous and autonomous optimization for Dataproc workloads leading to more efficient data analytics, data science and machine learning

One optimization solution for all Dataproc use cases

Achieve faster, more efficient Dataproc 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 EMR jobs in less time

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

Enhanced Dataproc orchestration

Maximize throughput per node by optimizing orchestration, leading Dataproc’s autoscaler to reduce cluster size accordingly.

Spark dynamic allocation

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

JVM execution for Spark

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

“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%!”