Why Data Engineers Use Parquet Instead of CSV
If you open any data engineering job description, you will see Parquet listed under required skills. Yet most beginners start with CSV files. Understanding why the industry switched to Parquet — and the specific technical reasons behind it — is one of the most important foundational concepts in data engineering.
What is wrong with CSV?
CSV files are human-readable, simple, and universal. So why does every production data pipeline avoid them?
The problem is how CSV stores data. CSV is row-oriented — each row is written together sequentially. To answer the query SELECT SUM(revenue) FROM sales, a CSV reader must scan every single column in every single row, even though you only need the revenue column.
For a file with 100 columns and 10 million rows, that means reading roughly 100x more data than necessary. At scale, this becomes the difference between a query running in 3 seconds or 5 minutes.
What Parquet does differently
Parquet is columnar — it stores all values for each column together. To read the revenue column, Parquet reads only the revenue column data and skips everything else.
This single change has dramatic effects on query performance and storage costs:
- Queries that touch 5 columns out of 100 read 95% less data
- Columns with similar values compress extremely well (revenue values like 99.99, 100.00, 99.99 compress much better than mixed row data)
- Typical CSV to Parquet compression: 5x to 10x smaller file size
Predicate pushdown — the second advantage
Parquet stores metadata about each row group — the minimum and maximum value of each column inside that group. When you run WHERE order_date = 2026-01-15, Parquet reads this metadata first and skips any row group where the min and max dates do not include January 15th.
This is called predicate pushdown, and it is one of the reasons partitioned Parquet tables on cloud storage can query billions of rows in seconds.
CSV has no such metadata — the reader must scan every row to find the matching ones.
When CSV is still fine
CSV is appropriate when:
- The file is small (under a few hundred MB)
- A human needs to open and read it directly
- You are exchanging data with a non-technical system that only accepts CSV
- You are doing a one-time data migration
For everything else — production pipelines, data lakes, analytical tables — use Parquet. Your queries will be faster, your storage costs will be lower, and your pipeline will behave predictably at scale.
Parquet on Azure, AWS, and GCP
All three cloud platforms treat Parquet as the default format for data engineering workloads:
Azure: ADLS Gen2 + Databricks use Parquet as the underlying format for Delta Lake tables
AWS: S3 + Glue + Athena are optimized for Parquet — Athena charges per byte scanned
GCP: Cloud Storage + BigQuery external tables work natively with Parquet
When you write a Delta Lake table in Databricks, you are writing Parquet files with a Delta transaction log on top. Understanding Parquet means understanding what Delta Lake, Iceberg, and Hudi are built on.