Intelligent resource allocation
Intel Tiber App-Level Optimization’s solution tailors scheduling and prioritization decisions regarding CPU, locks, caches and memory to improve your application-specific infrastructure’s performance.
Real-time continuous optimization of data management operations
Supports all major CSPs
Accelerate data streaming. Reduce processing time.
35%
Average Response
Time Reduction
Time Reduction
20%
Average Big Data
Cost Reduction
Cost Reduction
0
Code Changes
“With the implementation of Intel Tiber App-Level Optimization, American Airlines reduced the number of utilized nodes and got more headroom. This freed up the engineering teams to process and analyze data at the pace and scale that they needed. In short, data teams are now able to use Data Lake as the platform meant for it to be used.”
Vijay Premkumar, Sr. Manager of Cloud Innovation
From the session - American Airlines Optimization Journey around big data AI with Intel
Intel Tiber App-Level Optimization optimizes Databricks’ data management operations
Intel Tiber App-Level Optimization envisions a future where data management and sharing are not just open and collaborative, but also characterized by unmatched performance and cost efficiency. This collaboration signals a step toward making this vision a tangible reality, combining Intel Tiber App-Level Optimization’s autonomous optimization capabilities with the open data management facilitated by the Databricks platform.
Seamlessly reduce Databricks costs
Autonomous optimization to enable budget allocation to new data streams
while continuously preventing wasted resources
Runtime Optimization
Improve job completion times, reduce CPU, and increase throughput, for faster job completion time and more efficient data processing
Dynamic Capacity Management
Cut costs, streamline governance, and optimize workloads through node and DBU reduction
Seamless Scaling
Whether you use fixed clusters or utilize Databricks autoscaling
Complete more Databricks jobs faster
Continuously adapt resources and runtime environments to application workload patterns
Spark Optimization
JVM Runtime Optimization
Memory Arenas Optimization
Compression & Serialization Acceleration
Improve performance, reduce costs
Easy and autonomous deployment
Intel Tiber App-Level Optimization’s solution can be deployed with no code changes in minutes, leading to performance improvements, increased throughput and up to 45% cost savings on Databricks workloads in just days.
Enhanced dynamic autoscaling
The solution complements Databricks’ autoscaler by autonomously customizing each individual workload’s provisioned resources, improving performance and enabling engineering teams to process and analyze data at the pace and scale they need.
Get started in minutes
Install
Quickly install Intel Tiber App-Level Optimization on your Databricks applications
Evaluate
Let Intel Tiber App-Level Optimization autonomously learn the data application and workloads
Activate
Activate Intel Tiber App-Level Optimization and immediately experience Databricks performance improvements and cost reduction
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