In today’s world, data is being generated at an exponential rate. To process this data, companies need robust platforms that can scale to meet their requirements. Databricks is one such platform that provides a managed Spark service, allowing organizations to scale their big data processing capabilities without having to worry about the underlying infrastructure. 

Download this guide to explore the challenges, best practices and background knowledge necessary for optimizing Databricks and reducing your cloud costs.

Databricks is an excellent platform for big data processing needs. However, optimizing Databricks usage is essential to ensure that you are not overspending on infrastructure costs. By following the best practices outlined in this guide and using Intel Tiber App-Level Optimization’s Big Data solutions, you can optimize your Databricks usage and save costs.

Learn How to Optimize Your Databricks Workloads and Explore

The State of Databricks

Discover the trends that are influencing Databricks and how its ease of use can come with unique optimization challenges.

Get to Know Databricks Architecture

Read this clear and straightforward breakdown of Databricks’ wide range of use cases, components and features.

Databricks on AWS, Azure and GCP

Explore the various optimization challenges and advantages that come with each of the leading cloud providers.

Resolve Common Databricks Performance Issues

Dive deep into the optimization strategies data skew, inadequate caching, inefficient queries, and more.

Databricks Workloads with Autonomous Optimization

Find out how continuous, autonomous optimization fits into your Databricks streaming and processing strategy.