September 10, 2024

Optimizing Data Pipeline Performance with Databricks

The availability of Databricks tools to enhance and optimize performance of data pipelines can be intimidating, but leveraging these tools correctly is paramount to the cost containment and efficient management of your data estate.

data pipeline

When first adopting a data + AI platform, many organizations take a Life + Shift approach to migrating pipelines and workloads. Unfortunately, this can sometimes result in costly data pipelines with lengthy run times. Fortunately, Databricks provides a myriad of options to optimize existing Databricks pipelines, as well as enhancing pipelines from other sources when migrating to Databricks.

Data Pipeline Optimization Options

The robustness of the Databricks platform allows for a variety of options to optimize your pipelines, some will be a better fit for your pipelines than others. Here, in no particular order, we have listed five of Entrada’s favorite Databricks methods to support pipeline optimization:

  • Liquid Clustering: A feature that automatically determines the best data layout for your pipelines, reducing run times and cost
  • Cluster Settings: The UI that enables updates to compute configuration that can result in more performant pipelines
  • Photon: A vectorized engine that runs workloads faster to reduce cost per workload
  • Partition Strategy: An optimized partition strategy that reduces the number of files that need to be read, resulting in faster processing times
  • Z Ordering: A process that uses colocation on data files for data skipping to reduce the amount of data that needs to be read

Customer Success Story 

The Challenge:

A large sales automation software platform partnered with Entrada to support their objectives of maximizing the performance of legacy Databricks pipelines, and evaluating the case for the migration of EMR streaming jobs.

The Solution: Entrada provided a detailed set of performance related findings and ROI recommendations related to the client’s infrastructure for over 300 data pipelines. These findings were then prioritized with the remediation implemented by Entrada:

  • Migrated the highest cost and lowest performing EMR pipelines to Databricks
  • Optimized high cost and low performing Databricks pipelines
  • Leveraged a combination of Databricks capabilities (i.e. Liquid Clustering, cluster settings, and advanced partitioning) to provide a more performant experience with the client’s lakehouse pipelines
  • Migrated pipelines to Unity Catalog to enable future AI initiatives
  • Optimized pipelines to be DLT compliant

The Results: 

Through strategic collaboration with Entrada, the client successfully enhanced the performance and cost-efficiency of its data infrastructure and experienced: 

  • 72% cost savings in run cost for largest pipeline
  • >82% reduction in initial load time
  • >50% reduction in run time on largest tables

The availability of Databricks tools to enhance and optimize performance of data pipelines can be intimidating, but leveraging these tools correctly is paramount to the cost containment and efficient management of your data estate. Entrada’s experts can help your business enhance and optimize existing Databricks workloads, as well as migrate poor performing workloads to Databricks for a healthier data ecosystem.

About Entrada

Entrada is a Databricks-focused consulting and implementation partner backed by Databricks Ventures. Entrada harnesses the power of Databricks to help customers accelerate their AI + data initiatives. Our expertise in AI/ML, Databricks, and analytics is centered around industry-centric solutions. Our mission is to simplify complex data + AI challenges and support end-to-end transformations, delivering future-ready solutions fast.

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