Name, Age, City
Alice, 25, Chennai
Bob, 27, Delhi

Hey folks 👋,

As I kept building more data pipelines, I noticed one file format showing up everywhere: Parquet.

Every tool supported it. Every data engineer recommended it. Every project used it. But I still had one question stuck in my head:

Why is Parquet so fast - and why does every modern data stack rely on it?

So I dug in. Not just to use it, but to understand it. Here’s the breakdown 👇

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🧱 Row vs Column - The Core Difference

Most of us start with simple formats like CSV or JSON. They’re easy to read and quick to work with - but they hit limits fast.

How row-based formats store data (CSV/JSON):

Name, Age, City
Alice, 25, Chennai
Bob, 27, Delhi

Great when you need all columns for a few rows.

Terrible when you need one column from a million rows - you still have to read everything.

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Parquet flips this idea. It stores data column-wise:

Name → [Alice, Bob]
Age  → [25, 27]
City → [Chennai, Delhi]

This small shift changes everything:

• You read only the columns you query 🔍
• Similar values sit close together → compression works better
• Encodings (dictionary, bit-packing, RLE) become super efficient

This alone gives Parquet a massive performance edge.

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🧭 Metadata + Offsets = The Secret Weapon

Here’s the part that impressed me the most.

A Parquet file doesn’t just store your data. It also stores:

• rich metadata
• byte offsets
• column chunk locations
• statistics (min, max, null count)

This allows engines like Spark, Trino, DuckDB, and ClickHouse to:

👉 Skip scanning the entire file 👉 Jump directly to the byte ranges containing the required columns 👉 Avoid reading unnecessary blocks

Think of it like opening a book exactly to the paragraph you need - no flipping pages.

And in cloud storage (S3 / GCS / Azure Blob), this is gold. You can fetch only a tiny slice of a massive file.

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🧪 Where Parquet Really Shines

Once your dataset grows past a few MBs, Parquet starts showing its strength - and when you hit GBs or TBs, it becomes almost essential.

In one of my ingestion pipelines, we processed hundreds of MBs of Parquet files before loading them into ClickHouse. Even with selective column reads, the performance was consistently fast.

Why?

• Analytical workloads = more reads than writes
• Queries usually touch only a few columns
• Compression reduces storage + network cost
• Encodings reduce CPU cost

Parquet is literally built for this world.

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🔍 Offsets and Metadata in Real-World Code

When you read Parquet using:

• Python (PyArrow / Pandas)
• Go (Arrow Go)
• Spark
• DuckDB

You don’t manually deal with offsets.

The reader library automatically:

1. Reads file metadata
2. Figures out which column chunks are needed
3. Jumps to those byte ranges
4. Loads them efficiently (often vectorized)

This results in:

• Lower I/O
• Faster cloud reads
• Easy parallelization
• Better CPU efficiency

Those tiny metadata blocks inside the file? They’re the hidden reason your queries feel instant.

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💭 Closing Thoughts

Understanding why Parquet is fast made me appreciate something important:

In data engineering, performance often comes from how you store data - not how you process it.

Frameworks, pipelines, and orchestration get the spotlight, but formats like Parquet silently power the entire analytics ecosystem.

Next, I’m planning to dive into:

• Predicate pushdown
• Vectorized reads
• How query engines execute scans

Because that’s where things get even more interesting 👇

Until then - if you ever wondered why Parquet is everywhere, now you know why it deserves the hype 💾

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✍️ About Me

Mohamed Hussain S Associate Data Engineer

🔗 LinkedIn • GitHub