Apache Spark
Distributed computing engine for large-scale data processing and machine learning.
Best T-Factor
Transformation
T3
Weakest T-Factor
Trust
T5
Architectural Position
Processing and compute layer.
Objective Description
Apache Spark is an open-source distributed computing engine designed for large-scale data processing. It provides APIs in Python (PySpark), Scala, Java, and R, and supports batch processing, streaming (Structured Streaming), SQL queries (Spark SQL), machine learning (MLlib), and graph processing (GraphX). Spark operates on in-memory computation, significantly reducing I/O compared to MapReduce.
Architectural Position
Processing and compute layer. Positioned between storage (HDFS, S3, ADLS, GCS) and downstream consumers (warehouses, ML serving, reporting). Commonly embedded within Databricks, EMR, or HDInsight managed services.
Use Case Fit
When to Use
- Large-scale batch processing workloads exceeding single-machine capacity
- Complex transformations requiring distributed computation across structured and unstructured data
- ML feature engineering at scale requiring distributed processing
- Unified batch and streaming processing within a single framework
When NOT to Use
- Small to medium datasets where single-machine tools (pandas, DuckDB) are sufficient
- Simple SQL analytics workloads better served by a data warehouse
- Teams without distributed computing expertise to tune and operate Spark jobs
- Low-latency operational queries — Spark is optimized for throughput, not latency
Anti-Patterns
Common misuse scenarios and overengineering risks.
Using Spark for datasets that fit in memory on a single machine — overhead exceeds benefit
Collecting large DataFrames to the driver node, causing out-of-memory failures
Ignoring data skew in partitioning, leading to stragglers and job failures
Treating Spark as a governance layer — it is a compute engine, not a data platform