Optimizing Spark Jobs on Azure Databricks: Practical Tips for Speed and Savings

Azure Databricks is a powerhouse for big data processing, combining the best of Apache Spark with the scalability of Azure. But as any data engineer knows, if not tuned properly, Spark jobs can quickly become slow, expensive, or both.

The good news? A few smart adjustments can dramatically improve performance and control costs.

In this post, we’ll walk through three critical strategies that every Spark user should have in their toolkit: caching, partitioning, and cost optimization.

1. Caching:

Why Caching Matters

Apache Spark is built around lazy evaluation—it doesn’t compute anything until an action (like .count() or .collect()) kicks in. If you use the same DataFrame multiple times, Spark recalculates everything from scratch unless you cache it.

How to Cache a DataFrame

df.cache()

df.count()  # Triggers materialization

That .count() call is key—it forces Spark to cache the data in memory.

When to Cache

• Reused DataFrames: When the same dataset is accessed in multiple steps or joins.
• Iterative Workflows: Like ML model training loops.
• Heavy Aggregations or Joins: Repeated operations on the same input.

When Not to Cache

• Large Datasets: If your data doesn’t fit in memory, Spark spills to disk, defeating the purpose.
• One-Time Use: No need to cache if the DataFrame is only used once.

2. Partitioning:

What Is Partitioning?

Partitioning is Spark’s way of splitting data across tasks for parallel processing. Partition in the right way, it keeps workloads balanced and minimizes unnecessary data movement. If you partition the wrong way, it creates bottlenecks.

Types of Partitioning

1. Repartitioning – For Even Distribution

df = df.repartition(100)  # Triggers a full shuffle

Use this when you want to balance workloads across your cluster before expensive operations.

1. Coalescing – For Efficient Reduction

df = df.coalesce(10)

Perfect before writing output to avoid a many of tiny files.

1. Partitioning on Write – For Fast Queries

df.write.partitionBy(“country”).parquet(“/mnt/output”)

Organizing data by columns like country or date makes future queries faster.

3. Cost Optimization Tips for Azure Databricks

Performance is important—but so is keeping cloud costs in check.

Here are three easy ways:

1. Use Spot and Reserved VMs Wisely

• Spot VMs: Great for dev/test. Big savings but can be interrupted.
• Reserved Instances: Ideal for predictable, long-running jobs.

Configure these in your cluster settings.

Cluster settings > Performance  > Advance performance > Use Spot Instances        

1. Enable Auto-Termination

Idle clusters drain your wallet. Set an auto-termination timeout (like 10 minutes) so unused clusters shut down automatically.

Configure these in your cluster settings.

Cluster settings > Performance  > Terminate after 10 minutes of inactivity

1. Use Delta Tables Instead of Plain Parquet

Delta Tables bring massive performance and cost benefits:

• Support for ACID transactions
• Efficient upserts and updates
• Z-Ordering for faster filtering

df.write.format(“delta”).save(“/mnt/delta/sales”)

They also integrate better with Unity Catalog and enable powerful features like Time Travel and schema enforcement.

Final Thoughts: Small Tweaks, Big Impact

Optimizing Spark jobs in Azure Databricks doesn’t have to be overwhelming. With just a few thoughtful adjustments like caching smartly, partitioning strategically and keeping costs under control, you can transform slow, expensive pipelines into fast, scalable data workflows.

What About You?

Have you discovered a clever optimization trick in Azure Databricks? Faced challenges with caching or partitioning? Drop your thoughts in the comments below!

Databricks Courses page – https://www.cloudthat.com/training/databricks

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