Optimizing Apache Hudi & Spark Pipelines to Achieve 10X Performance at 1/2 the Cost

Apache Hudi is used by many of the world’s most demanding data teams to manage mutable data at scale. Spark in its various forms (open source, EMR, Databricks) is the processing engine of choice for running Hudi pipelines, whether ingesting large volumes of streaming data with minute-level freshness or processing complex ETL pipelines.

To get the most out of your Hudi lakehouse, it’s important to optimize the pipelines, tables, and the underlying Spark compute environment for performance and costs. Onehouse has worked with many Hudi users to achieve 2X - 30X query performance improvements while reducing infrastructure costs by 20% - 80%.

Join this webinar to learn how Onehouse can help you optimize your own lakehouse through a combination of:

• Onehouse Compute Runtime, which provides a compute environment optimized for lakehouse workloads and Spark. It combines serverless and dynamically scalable compute, adaptive workload optimizations, and high-performance lakehouse IO.
• Onehouse Table Optimizer, which continuously automates your tables through incremental clustering, async compaction, and advanced cleaning, ensuring peak performance at all times.
• Expert services to assess and optimize the configuration of your Hudi tables. Our team has years of experience working on complex and demanding use cases.

Whether you are building your first Hudi lakehouse or need to squeeze more out of an existing one, join us to learn how Onehouse can help you achieve better results for a fraction of the cost.