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Optimizing AI: Large-Scale Data Processing and Analytics

Alon Berger

Product Marketing Manager, Intel Granulate

In part two of our ongoing series on Optimizing AI workloads, we’re going to dive into how autonomous performance optimization can benefit Large-scale Data Processing and Analytics, and accelerating throughput while managing costs. If you missed our previous discussion on Machine Learning, catch up here.

Recently, there have been unprecedented investments in data processing and analytics, and that number is only expected to increase rapidly. The market size value for data analytics in 2023 was over $57 billion and the revenue is forecast to surpass $300 billion by 2030. However, much of those costs may be a result of enterprise overspend.

Just like with any other Big Data application, data processing and analytics workloads can become inefficient over time. When attempting to reduce costs and improve performance through optimization, data engineering teams can face a number of challenges, including workload complexity, insufficient visibility, scalability issues and difficulties coping with the dynamic nature of these types of workloads.

Benefits of Optimizing Large-scale Data Processing and Analytics

Here are some of the benefits that IT teams responsible for data processing and analytics might take advantage of with the use of autonomous optimization:

Enhanced Throughput

Autonomous optimization leads to increased throughput, allowing for the processing of more data in less time. This improvement is crucial in large-scale data processing and analytics, where handling vast datasets efficiently can significantly speed up overall data analysis.

Reduced Processing Time

Optimizing CPU performance can significantly shorten processing times for complex data analytics tasks. By executing operations more efficiently, CPUs can complete data-intensive jobs faster, translating to quicker insights and decision-making in large-scale data environments.

Get the Big Data Optimization Guide

Improved Resource Efficiency

CPU optimization enhances the efficiency of resource utilization, ensuring that computational power is used more effectively. This leads to a reduction in operational costs and energy consumption, making large-scale data processing and analytics more cost-effective and sustainable.

Engineer Enablement

With the implementation of autonomous performance optimization, businesses can reduce the number of utilized nodes and achieve more headroom. This can free up engineering teams to process and analyze data at the pace and scale that they need. 

Intel Granulate Solutions for Large-scale Data Processing and Analytics

Big Data 

Intel Granulate efficiently handles complex workloads across various execution engines, platforms, and resource orchestrations. Its dashboard offers comprehensive visibility into data workload performance, resource utilization, and costs, allowing for effective monitoring and adjustment of optimization activities. 

The solution dynamically manages rapid scaling and fluctuating data characteristics, ensuring resource allocation is constantly updated for maximum efficiency, minimizing CPU and memory waste. This automation spares data engineering teams from manual adjustments, adapting to dynamic data pipelines, thus reducing compute costs and streamlining large-scale data processing operations. Learn more here.


Intel Granulate provides Spark and MapReduce optimization to increase density at lower costs by enabling dynamic Spark executor scheduling and YARN resource allocation with continuous optimization and node-level granularity of containers’ CPU and memory, and allocation and preemption of those containers. Specifically for Databricks, Dataproc and EMR, the solution optimizes managed scaling. 

Under a recent agreement, Intel Granulate’s suite of autonomous optimization solutions will be merged with Databricks’ robust Data Intelligence Platform by joining the Databricks Partner Program. Learn more here.

Databricks Optimization Guide Download Blog CTA

Runtime / JVM Optimization

For large-scale data processing and analytics applications, especially those dependent on Java Virtual Machines (JVM), runtime optimization can make a big difference. Intel Granulate’s solutions fine-tune JVM settings to ensure peak performance for these types of tasks.

When combined with the Big Data and Databricks optimizations, this improvement on the runtime level drives even more value. Learn more here.

“Intel Granulate offers a combination of different optimization tools. So, while we were hooked by the enhanced autoscaler for Data Lake orchestration, it was that added layer of runtime optimization that made the difference.”

Vijay Premkumar
Sr. Manager – Cloud Platform Innovation at American Airlines 

The Impact of Intel Granulate on CPU-Based Data Processing and Analytics 

Intel Granulate enhances large-scale data processing and analytics by optimizing CPU utilization, managing resources cost-effectively, improving scalability and elasticity, streamlining Spark operations, and significantly enhancing data processing speed.

When Intel Granulate is deployed on Big Data workloads, they experience an average of 31% reduced processing time and up to 45% compute cost reduction, through more efficient CPU utilization. With real-time, continuous orchestration, data engineering teams can avoid constant monitoring and benchmarking, allowing them to tune large-scale data processing and analytics workloads to their unique cost-performance sweet spot.

However, the impact of autonomous optimization doesn’t end at performance and cost. American Airlines spoke about their experience with Intel Granulate on their Data Lakes dedicated to data processing and analytics. Because Data Lakes has a limited number of node connections allowed, the reduced job completion time provided by Intel Granulate allowed their engineers to process and analyze data at an accelerated pace and with greater scalability. According to Vijay Premkumar, Sr. Manager – Cloud Platform Innovation at American Airlines, their “data teams are now able to use Data Lake as the platform meant for it to be used.”

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