Welcome to Day 11 of the Spark Mastery Series!
Today we discuss one of the most underrated but most impactful decisions in data engineering:
Which file format should you use in Spark?
Choosing the wrong format can make your job 10x slower and 5x more expensive. Let's break down the formats clearly.
🌟 CSV - “Easy but Slow”
CSV is the simplest, but it is NOT optimized for big data.
Drawbacks:
- No schema
- No compression
- No pushdown
- No columnar storage
- Slow read performance
Example:
df = spark.read.csv("data.csv", header=True, inferSchema=True)
Use CSV only in:
- Raw ingestion (Bronze layer)
- External source feeds
🌟 JSON — Great for Logs, Bad for Analytics
Pros:
- Handles nested structures well
- Useful for logs, API events
Cons:
- Slow
- Not columnar
- Weak compression
df = spark.read.json("logs.json")
🌟 Parquet — The King of Analytics
Parquet is a columnar, compressed, optimized format.
Why it’s the industry standard:
- Reads are fast
- Supports predicate pushdown
- Supports column pruning
- Highly compressed
- Splittable
df.write.parquet("path")
If you're building analytics or ETL, Parquet is your default format.
🌟 ORC — The Hadoop Favorite
ORC is similar to Parquet:
- Columnar
- Compressed
- Pushdown support
ORC is heavily used in:
- Hadoop
- Hive
But Parquet is more popular in cloud platforms like:
- Databricks
- AWS
- GCP
🌟 Delta Lake — The Modern Lakehouse Format
Delta = Parquet + ACID + Time Travel
Benefits:
- Atomic writes
- Schema evolution
- MERGE INTO support
- Faster ingestion
- Faster updates
- Versioned tables
Perfect for:
- Bronze → Silver → Gold ETL pipelines
- ML feature store
- Time travel debugging
df.write.format("delta").save("path")
🌟 How to Choose the Right Format?
Use CSV
→ Only for ingestion/raw dumps
Use JSON
→ Only for logs, events, nested data
Use Parquet
→ For analytics, BI, aggregations
Use Delta
→ For ETL pipelines, updates, deletes, SCD2
🌟 Real-World Example
For a retail data system:
`
Layer Format
Bronze (raw) CSV, JSON
Silver (cleaned) Parquet
Gold (analytics) Delta
`
This is exactly how modern Lakehouse systems are built.
🚀 Summary
We learned:
- Differences between file formats
- Why Parquet & Delta are fastest
- Why CSV & JSON hurt performance
- Pushdown & column pruning
- How file formats impact ETL pipelines
Follow for more such content. Let me know if I missed anything in comments. Thank you!!
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