How Switching from CSV to Parquet Saved Us 80% in Storage Costs Using Databricks

When we think of data storage, it's easy to focus on the upfront costs: hardware, cloud infrastructure, or the expense of data wrangling tools. But there’s one factor that often gets overlooked—data format.

It wasn’t until we made a strategic shift from CSV to Parquet that we truly understood just how much data format impacts our storage costs—and how switching to Parquet via Databricks helped save 80% in storage costs.

In this post, I’m sharing the story of our journey from CSV to Parquet, the challenges we faced, and the savings we unlocked.

The Problem: Data Bloat and Escalating Costs

I've been working with large datasets for several years, primarily storing our data in CSV files. At first, CSV seemed like an easy, accessible solution. After all, it’s simple, human-readable, and widely supported.

But as our data grew—spanning millions of records and tens of gigabytes—so did our storage costs.

We quickly learned that our CSV files were bloating, causing inefficiencies in both storage and performance. Every time we loaded or queried data, the performance was sluggish.

The files were large, uncompressed, and difficult to work with. Our cloud storage bill was climbing without us fully understanding why.

Here’s a breakdown of the issues:

• Large File Sizes: CSV files are stored as plain text, which leads to significant file size inflation, especially when dealing with large datasets.
• Storage Inefficiency: Each CSV file contained redundant information (e.g., column headers repeated in every row) and inefficient encoding for numbers, leading to wasted space.
• Slow Processing: As we scaled up, reading from and writing to large CSV files became an increasingly slow process, further straining our computing resources.

It became clear that we needed to optimize both our storage and processing workflows—but how?

The Solution: Switching to Parquet with Databricks

After exploring some possible solutions and reading up some documentations on Databricks, Parquet format was the solution, a columnar storage format known for its high compression ratio and performance benefits.

Parquet is designed to be efficient in both storage and retrieval. Its columnar format means that data is stored in columns rather than rows, making it highly optimised for analytical queries that only require specific columns.

This leads to:

1. Better Compression: Parquet files are highly compressed, meaning we could store much more data in the same amount of space. Its support for complex data structures (like nested data) further optimised our storage.
2. Faster Query Performance: By storing data in columns, Parquet allows us to read only the relevant parts of the data, improving performance dramatically—especially for big datasets and complex queries.
3. Cost Efficiency: With better compression and optimised storage, we saw an immediate reduction in storage usage, translating to lower costs in the cloud.

Table below shows a snapshot comparison between CSV files and Parquet Formats

The Results: 80% Savings in Storage Costs

The switch was a game-changer. We started by migrating a few of our large datasets from CSV to Parquet.

The results were almost immediate.

• Storage Savings: The reduction in file sizes was dramatic. By leveraging Parquet’s columnar storage format, we reduced the size of our dataset by as much as 80%. This translated directly into an 80% reduction in our storage costs.
• Faster Data Processing: Not only did storage costs decrease, but our data processing times also improved. Since Parquet only loads the columns we need, queries ran faster, and ETL processes were completed in a fraction of the time it took with CSV.
• Scalability: With this newfound efficiency, we were able to scale our data operations without worrying about skyrocketing storage costs or performance bottlenecks. As our data grew, we felt confident that we could handle the load with minimal additional investment.

Some great use cases that show that this is having a huge impact by some organisations:

1. nOps
2. WTD Analytics

The Bottom Line: Optimising Data Storage is Critical for Business Efficiency

Switching from CSV to Parquet may sound like a simple change, but it was one of the most impactful decisions I made in terms of data cost optimisation and performance improvement.

Here are a few key takeaways for anyone considering a similar move:

1. Data format matters: If you're working with large datasets, using a plain text format like CSV can quickly become inefficient. Columnar formats like Parquet are optimised for both storage and performance.
2. Leverage cloud-native platforms: Databricks made it easy to transition from CSV to Parquet, offering a seamless, cloud-based solution with automatic optimisations.
3. Cost savings are real: In our case, we saw an 80% reduction in storage costs. If your data is growing fast, optimising storage can have a significant impact on your bottom line.
4. Performance matters too: Lower costs are great, but performance improvements are equally important. With Parquet, we’ve seen faster data processing times that directly benefit our analytics and reporting.

In the end, this small change in data format has had a massive impact on the ability to scale efficiently. The savings, performance improvements, and overall efficiency boost have made all the difference.

I’d love to hear your feedback. 😊 How have you optimised storing your datasets, and which formats have worked best for you? 📊🚀

Next Week

I’ll be taking a quick look at how we can use this knowledge to convert CSV files into Parquet files. 📊🔄✨