Apache Spark Data Pipeline Design: 15 Trade-offs Every Data Engineer Should Understand

How to reason about Spark pipelines beyond simply choosing Kafka, S3, Iceberg, and Snowflake

Designing an Apache Spark data pipeline is not primarily about picking technologies.

It is about making trade-offs.

Should the pipeline be batch or streaming? Should you keep immutable raw data? How many partitions should Spark process? Should you pre-aggregate data? How do you make reruns idempotent? When does an optimization actually make the pipeline harder to operate?

A typical Spark data pipeline may look simple:

Data Source
    ↓
Ingestion
    ↓
Raw Storage
    ↓
Apache Spark
    ↓
Cleaned / Enriched Data
    ↓
Aggregations
    ↓
Serving Layer

The difficult part is deciding what happens at every arrow.

Here are the most important Spark data pipeline design trade-offs.

1. Spark Batch vs Spark Structured Streaming

This is usually one of the first decisions.

Batch

Data
 ↓
Storage
 ↓
Scheduled Spark Job
 ↓
Processed Data

Advantages:

• simpler recovery
• easier backfills
• simpler debugging
• usually lower operational complexity