topic Understanding Parquet File Storage for Large Datasets in Data Engineering

Understanding Parquet File Storage for Large Datasets

gowri_databrick — Mon, 07 Sep 2026 04:23:05 GMT

Hi everyone,

I’m learning about Parquet files and how they are used in Databricks for storing large datasets.

I’m trying to understand how column-based storage works in a practical situation.

For example, suppose an e-commerce company has 500 million order records containing customer details, product information, order dates, and payment information. If an analyst only needs order_date and order_amount to calculate daily sales, how does storing the data in Parquet help the system process this query efficiently?

I’d like to understand how Parquet storage works in this type of real-world scenario.

Thanks!

Re: Understanding Parquet File Storage for Large Datasets

Satyasai — Mon, 07 Sep 2026 04:40:42 GMT

Parquet's columnar architecture optimizes execution through three primary mechanisms: Column Projection, Page-Level Compression, and Metadata Pruning.
Row-Oriented vs. Column-Oriented Layout
To understand why Parquet is fast, compare how a traditional row-oriented format (like CSV or JSON) stores data on disk versus Parquet:
Row-Oriented Layout (CSV/JSON):
[Record 1: Customer, Product, Date, Amount, Payment] [Record 2: Customer, Product, Date, Amount, Payment] ...

Parquet Columnar Layout (Grouped into Row Groups):
[Row Group 1]
├── Column 1 (order_date): [2026-09-01, 2026-09-01, 2026-09-02, ...]
├── Column 2 (order_amount): [120.50, 45.00, 89.99, ...]
├── Column 3 (customer_id): [C102, C994, C102, ...]
└── Column 4 (payment_info): [Visa, Mastercard, Amex, ...]
The 3 Core Performance Advantages in Practice
1. Column Projection (Reading Only What You Need)
In a CSV or JSON file, to read order_date and order_amount for 500 million rows, the query engine must scan every single byte of the file from start to finish—including heavy text fields like customer addresses and payment tokens—just to discard them in memory.
In Parquet, data for each column is stored sequentially in contiguous disk blocks.
• When Databricks executes SELECT order_date, order_amount FROM orders, the query engine performs Column Projection.
• It completely skips the byte locations on disk where customer_id, product_info, and payment_info reside.
• If those unused columns represent 80% of your row width, your total I/O drops by ~80% instantly.
2. Homogeneous Compression Ratios
Compression algorithms (like Snappy or ZSTD) perform best when repeating, similar patterns of data are adjacent to one another.
• Row-oriented files mix numbers, timestamps, long strings, and booleans together in every byte block, making high-ratio compression difficult.
• Parquet columns group identical data types together. An entire column block contains only dates (2026-09-01), or only floating-point amounts.
Parquet applies specialized encodings directly to column data before compressing:
• Dictionary Encoding: Replaces repeating text values with short integer keys.
• Run-Length Encoding (RLE): Stores repeated values efficiently (e.g., storing "2026-09-01 repeated 50,000 times" as a compact tuple).
This drastically reduces the physical footprint on cloud storage, allowing Databricks to pull much smaller files across the network.
3. Row Group Metadata & Data Skipping
A Parquet file is divided into Row Groups (typically containing 100,000 to 1,000,000 rows each). Every Parquet file contains a Footer with rich metadata.
For every Row Group, the Parquet footer records:
• Minimum and maximum values (min_val, max_val) for each column.
• Total null counts and physical byte offsets.
Parquet File Footer Metadata:
Row Group 1: order_date [Min: 2026-01-01, Max: 2026-03-31]
Row Group 2: order_date [Min: 2026-04-01, Max: 2026-06-30]
Row Group 3: order_date [Min: 2026-07-01, Max: 2026-09-30]
If your daily sales query includes a filter like WHERE order_date >= '2026-09-01', the Databricks engine reads the file footer first. It sees that Row Groups 1 and 2 contain no data for September 2026 and skips reading those row groups entirely.
Databricks Delta Lake Layer
While standard Parquet provides these column-level benefits, Delta Lake (the default table format in Databricks) builds an additional transaction log layer (_delta_log) on top of Parquet files. Delta Lake tracks file-level statistics across millions of Parquet files, enabling Databricks to skip entire files without even opening their footers.

Re: Understanding Parquet File Storage for Large Datasets

balajij8 — Mon, 07 Sep 2026 05:27:48 GMT

@gowri_databrick

Parquet's columnar storage organizes data by column rather than by row - all values for order_date are stored together, all order_amount values are stored together and so on. In your commerce scenario with 500 million records, when the analyst queries only order_date and order_amount, Databricks reads only those two columns from disk completely skipping the customer details, product information and payment data. This is fundamentally different from row-based formats like CSV or JSON where the entire row must be read even if you need just two fields. With Parquet, if those two columns represent only 10% of the total data width you are reading 90% less data from storage.

This columnar approach delivers three major benefits on Databricks - dramatically faster queries (reading 100MB instead of 1GB is a massive speed improvement), lower compute costs (less data to process leads to less CPU and memory usage) and reduced storage costs through great compression (values in a single column tend to be similar so order_date values like 2024-01-15 compress much better when stored together than scattered across row records). Query optimizer automatically leverages Parquet's column pruning and predicate pushdown, so filtering on order_date > 2024-01-01 reads only the relevant row groups making analytics on massive datasets fast and cost efficient.

Re: Understanding Parquet File Storage for Large Datasets

data_pulse — Mon, 07 Sep 2026 06:36:33 GMT

@gowri_databrick

The easiest way to understand Parquet’s column-based storage is with a small example:

order_id | customer | order_date | order_amount | payment 1 | Alice | 2026-09-01 | 100 | Card 2 | Bob | 2026-09-01 | 50 | Cash 3 | Carol | 2026-09-02 | 300 | Card

In above dataset, row-orient formats like csv, the data is stored roughly as:

Row 1 → all columns
Row 2 → all columns
Row 3 → all columns

Parquet is organized into row groups, and within each row group the values are stored in separate column chunks:

order_id → 1, 2, 3
customer → Alice, Bob, Carol
order_date → 2026-09-01, 2026-09-01, 2026-09-02
order_amount → 100, 50, 300
payment → Card, Cash, Card

Now imagine the table has 500 million orders and the analyst runs queries like

SELECT order_date, SUM(order_amount) FROM orders GROUP BY order_date;

The query doesn't need customer, payment, product_id, etc. Because Parquet is columnar, the engine can mainly read the order_date and order_amount column chunks instead of reading all columns.

Benefit:

Row format: read a lot of unnecessary data → discard unused columns
Parquet: read the columns the query actually needs.

Note: column pruning and data skipping are different things.

Column pruning → avoids reading unnecessary columns.
Data skipping → can avoid reading files/row groups that cannot contain the required rows, based on statistics.

So with a large Databricks table, the simplified flow is:

Find relevant files → skip irrelevant data → read only required columns → process the result.

Parquet itself provides the columnar file format, compression/encoding, row groups and statistics. Delta Lake adds the transaction log and file-level statistics/data skipping on top of those Parquet files.

Re: Understanding Parquet File Storage for Large Datasets

srini_ve — Mon, 07 Sep 2026 17:02:25 GMT

@gowri_databrick

Yes, this is a good example to understand why Parquet is useful.

In your scenario, imagine the 500 million order records are stored in Parquet with columns like:

customer_id | product_id | order_date | order_amount | payment_type

If the analyst runs:

SELECT order_date, SUM(order_amount)
FROM orders
GROUP BY order_date;

Databricks doesn't need to read all the columns such as customer_id, product_id, or payment_type.

Because Parquet stores data by column, Spark can mainly read just the order_date and order_amount columns needed for the query. This means less data is read from storage and less data needs to be processed.

For example, instead of reading the entire 500-million-row dataset with all 5 columns, Spark can focus on the two required columns.

So, the simple takeaway is:

Parquet → Column-based storage → Read only the required columns → Less I/O → Better query performance

Re: Understanding Parquet File Storage for Large Datasets

Islam_hoti — Mon, 07 Sep 2026 19:48:51 GMT

Hey ,

Parquet stores data column by column instead of row by row, so a query only reads the columns it asks for. If your table has 20 columns and the analyst needs just order date and order amount, Parquet reads those two and skips the rest. CSV would have to read all 500 million rows in full.

It also compresses much better, since values of the same type sit together, and it keeps min and max stats per column so Spark can skip entire chunks that do not match your filter.

Worth noting that Delta sits on top of Parquet and adds file level statistics, so on Databricks you usually want Delta rather than raw Parquet.

Re: Understanding Parquet File Storage for Large Datasets

Coffee77 — Tue, 08 Sep 2026 18:47:43 GMT

Here is a summary on how parquet works in Databricks:

Besides you have here how traditional partitioning vs liquid clustering work:

I explain how it exactly works in this video but for now texts only in Spanish ...

Re: Understanding Parquet File Storage for Large Datasets

Khasim_1 — Wed, 09 Sep 2026 03:31:48 GMT

Hi Gowri,

The efficiency of Parquet in your e-commerce scenario comes down to two key architectural mechanisms: Columnar Projection and Data Skipping.

1. Columnar Projection (Reducing I/O): In a traditional row-based format (like CSV), the database must read the entire record—including customer details, product descriptions, and payment info—just to access the order_date and order_amount. That is a massive waste of I/O. Because Parquet is columnar, the engine physically only reads the files associated with the order_date and order_amount columns. It completely ignores the other columns on the disk, which reduces the amount of data read by potentially 80-90% in a wide table.

2. Data Skipping (Metadata Optimization): Parquet stores metadata (min/max values) at the file and row-group level. If your analyst filters by a specific date range, the Databricks engine reads the file metadata first. If the order_date range in a specific Parquet file doesn't match the query, the engine skips the file entirely. It never even opens it.

When we move to the Lakehouse, we often combine this with Delta Lake. With Delta, we add a transaction log on top of these Parquet files, which allows for Z-Ordering or Liquid Clustering. By co-locating similar data, we make that "data skipping" even more aggressive.

For your 500-million-record example, the difference is night and day: Row-based processing would be scanning terabytes, while Parquet/Delta allows you to scan only the necessary columns and skip irrelevant files—turning what would be a multi-minute job into a sub-second response.

Hope this helps!

Re: Understanding Parquet File Storage for Large Datasets

Coffee77 — Wed, 09 Sep 2026 06:30:43 GMT

In addition to my previous picture, I'd add the following explanation. In Databricks, Partition Pruning, Data Skipping and Parquet’s columnar format work together to minimize I/O.

1. Partition Pruning → eliminates partitions.
If the table is partitioned by date and the query uses WHERE date = '2026-09-09', Databricks ignores all other partitions and their files. This step reduces largely from the very beginning the number of files (and data) to read.

2. Data Skipping → eliminates Parquet files.
Delta Lake uses file-level statistics such as min/max values to discard files that cannot contain the requested data. This is the second level to narrow data to read.

3. Column Pruning → reads only required columns.
Parquet stores data by columns rather than by rows. Once the relevant files are selected, Databricks reads only the columns required by the query instead of the complete rows.

So, the flow would look like something similar to this: Query → Partition Pruning → Data Skipping → Parquet Files → Required Columns → Data

I hope it helps.

Re: Understanding Parquet File Storage for Large Datasets

Louis_Frolio — Wed, 14 Oct 2026 08:38:50 GMT

Greetings @gowri_databrick,

The thread has already covered a lot of good ground, so credit first:

@Satyasai walked through the layout, the encodings (dictionary and RLE), and the footer min/max stats.
@data_pulse made the key point that column pruning and data skipping are separate optimizations.
@Coffee77 laid out the order things happen in: prune partitions, skip files, read only the needed columns.
@Khasim_1 and @Islam_hoti noted that on Databricks you'll usually be using Delta Lake on top of Parquet rather than raw Parquet files.

I'd add a few things, starting with your exact query.

Your daily sales query has no WHERE clause, so every order contributes to the result. Nothing gets skipped, and the engine scans order_date and order_amount across every file. The savings come from column pruning plus compression: it reads two narrow column chunks and ignores customer, product, and payment data. Parquet cuts down what you read, though it doesn't make a full aggregation free.

Add a filter and things change:

SELECT order_date, SUM(order_amount)
FROM orders
WHERE order_date >= '2026-09-01'
GROUP BY order_date;

Now Delta Lake can use per-file stats to skip files that can't match, and partition pruning can drop whole partitions (but only if the table is actually partitioned on that column). Then column pruning kicks in on whatever's left.

One clarification on the physical picture: columns don't live in separate files. Each Parquet file is split into row groups, and each row group holds one contiguous chunk per column. The footer records where each chunk starts, so the reader grabs the footer, then pulls only the byte ranges it needs from the same file.

A few practical things that decide whether skipping actually helps:

Sort order matters. Min/max stats only help when each file covers a narrow range of dates. If orders were written in random order, every file would span the whole year and a date filter would skip nothing. Liquid clustering on order_date keeps similar dates together, and it helps compression too since repeated values sit next to each other.
The filter column needs stats. For Unity Catalog external tables, Delta collects stats on the first 32 columns by default, so a filter on column 40 can't skip anything unless you reorder the schema or set delta.dataSkippingStatsColumns. Unity Catalog managed tables with predictive optimization pick stats columns for you, without the 32-column limit.
Go easy on partitioning. Databricks now recommends liquid clustering for all new tables and recommends not partitioning tables under a terabyte. Partitioning 500 million orders by day could leave you with lots of small partitions.

Last thing: the real gain depends on file sizes, stats, data layout, filter selectivity, and the query plan. Treat specific numbers like "80 to 90% less I/O" or "sub-second" in this thread as illustrations, not guarantees. If you want to see it for yourself, run the query with and without the date filter and compare files read versus pruned in the query profile.

At the end of the day: columnar storage trims what you read sideways (fewer columns), and stats plus clustering trim what you read lengthwise (fewer files). You need a filter and well-organized data to get the second one.

References:

Apache Parquet concepts: https://parquet.apache.org/docs/concepts/
Apache Parquet file format: https://parquet.apache.org/docs/file-format/
What is Delta Lake?: https://docs.databricks.com/aws/en/delta
Data skipping: https://docs.databricks.com/aws/en/tables/data-skipping
Liquid clustering: https://docs.databricks.com/en/tables/clustering.html
When to partition tables: https://docs.databricks.com/en/tables/partitions.html

Hope this helps.

Regards, Louis.

Re: Understanding Parquet File Storage for Large Datasets

Satyasai — Thu, 15 Oct 2026 05:54:36 GMT

Thank you for the information