Getting Started with Apache Spark on AWS EMR

Why Spark on EMR?

Apache Spark is the de facto standard for large-scale data processing. AWS EMR gives you a managed Spark cluster without the operational overhead of running your own Hadoop infrastructure.

Setting Up Your First Cluster

Start with a minimal cluster — you can always scale up:

aws emr create-cluster \
  --name "spark-demo" \
  --release-label emr-7.0.0 \
  --applications Name=Spark \
  --instance-type m5.xlarge \
  --instance-count 3

Optimizing Shuffle Partitions

The default spark.sql.shuffle.partitions is 200, which is almost always wrong:

spark.conf.set("spark.sql.shuffle.partitions", "50")  # tune to ~2x your core count

Key Takeaways

• Start small, profile first
• Tune partitions before anything else
• Use Spot instances for worker nodes to cut costs 60-70%