Apache Spark for Data Engineers: ETL Patterns that Scale
Apache Spark is one of the most popular frameworks for large-scale data processing. It shines when you need to transform terabytes of data, join many data sources, or run complex machine learning workloads.
Why Spark?
- Distributed – Automatically parallelizes work across a cluster.
- In-memory – Caches data to speed up iterative algorithms.
- Multi-language – Supports Python, Scala, SQL, and Java.
- Ecosystem – Includes Spark SQL, Structured Streaming, MLlib, and more.
Classic ETL pattern with Spark
A common pattern for data engineers looks like this:
- Extract – Read raw data from Kafka, files, or databases.
- Transform – Clean, normalize, and enrich data.
- Load – Write curated tables to your data lake or OLAP engine.
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("etl").getOrCreate()
df = spark.read.json("s3://bucket/raw/events/")
clean = df.filter(df.event_type.isNotNull())
clean.write.mode("overwrite").parquet("s3://bucket/curated/events/")
Structured Streaming
Spark’s Structured Streaming lets you treat streaming data like a continuously updating table. You write normal SQL-like queries, and Spark handles the incremental updates.
Use it when you need near real-time pipelines but want to stay in the Spark ecosystem instead of adding a separate stream processor.
Best practices for Spark ETL
- Use partitioning on date or other high-level keys to speed up queries and reduce cost.
- Compact many small files into fewer larger ones to avoid the “small files problem”.
- Push as much logic as possible into Spark SQL for easier maintenance.
- Monitor job runtimes, shuffle volume, and skew to keep clusters healthy.
Spark isn’t the only tool in the big data toolbox, but a solid understanding of Spark ETL patterns will prepare you for most large-scale data engineering problems.