July 24, 2025
Author: Kat Calejo
Do you ever open a report and wonder why it takes forever to load? Or maybe your cloud bill is creeping up and you’re not sure why. Most people don’t think twice about how their data is stored; they just assume the lag, the bloat, the endless waiting is normal. But what if we told you the problem isn’t your dashboard… it’s your file format?
If you’re still working with CSVs or JSONs, you might be slowing your whole operation down without realizing it. That’s where the parquet file comes in. It’s not new or flashy, but it is a smarter, faster, and more efficient way to store and move data, especially if you’re scaling or managing large datasets.
In this blog, we’re breaking down what parquet files are, how to know if you’re already using them (you might be), and how to start implementing them without blowing up your current setup.
Better performance shouldn’t require a total rebuild; it just needs the right tools.
What is a parquet file and why should you care?
Let’s break this down without the jargon.
When you open a traditional spreadsheet or CSV file, what you’re looking at is row-based storage. That means every time you want to access data, your system has to read full rows even if you only care about one tiny piece of information.
Say you have a file with a million customer records and you’re just trying to pull one field, like their zip code. With row-based storage, your system still has to sift through everything in every row just to get to that one detail.
Column-based storage (like what parquet uses) flips that model on its head. Instead of storing data by row, it stores it by column.
So now, if you only want zip codes? Your system skips all the names, addresses, emails, and everything else and goes straight to the column labeled “Zip Code.” It’s like skipping the whole buffet line and going right to the dish you came for.
This kind of efficiency matters when you’re working with large datasets, which– if your company collects anything like sales numbers, user behavior, or transactions– you probably are.
Parquet helps you extract the insights you need faster, using fewer resources, without slowing down the rest of your system.
So, how do you know if you’re already using parquet files?
Here’s the thing: a lot of teams are working with data every day without knowing how it’s being stored behind the scenes. Maybe your dev team is pulling analytics reports, maybe your cloud provider is backing up logs, maybe you’ve got a warehouse full of data and no idea what format it’s in.
Totally normal.
The fastest way to find out? Ask your team. Seriously, shoot a quick message to your developer, data analyst, or IT lead and ask: “Hey, are we using parquet files anywhere in our system?” If they know, they’ll tell you. If they don’t, that’s a pretty good sign it hasn’t been a focus (yet).
You can also look at where your data lives.
If you’re using platforms like AWS, Azure, or Google Cloud, there’s a decent chance parquet is already being used in some of the default tools. It’s the format of choice for services like Amazon Athena and Google BigQuery because it’s just that efficient. So even if you haven’t chosen it intentionally, it might already be part of your stack.
The bottom line? You don’t need to be an expert in file formats to benefit from them, but knowing whether or not you’re using parquet is the first step to making smarter, faster decisions with your data.
How to start using parquet files, even if you’re not technical
If you’re not already using parquet files, and now you’re thinking maybe you should be, good news: getting started doesn’t have to be a whole ordeal.
First, talk to whoever manages your data. Whether it’s a cloud engineer, a data analyst, or your IT partner, let them know you’re interested in improving performance and cutting storage costs by switching to parquet. This isn’t a total system overhaul; it’s usually just a matter of adjusting how data is exported, stored, or queried.
From there, it depends on your setup. If your team uses Python and tools like Pandas or Spark, converting files to parquet is as easy as changing one line of code. If you’re using cloud storage solutions like AWS S3 or Google Cloud Storage, you can often toggle parquet as your default format in the platform’s settings or during export.
And if you’re handling large datasets manually? You can still convert CSVs to parquet using free tools like Apache Arrow or DuckDB. It’s low lift, high return.
The key here isn’t to become a data engineer overnight. It’s to recognize that there’s a better, faster, smarter way to store and work with your data, and to loop in the right people to help you make that switch.
It’s small on effort, big on payoff.
How to start using parquet files without breaking what’s already working
If you’re still working with CSVs or JSON files and ready to level up, good news: you don’t need to burn everything down to get started with parquet.
Think of it like making your processes smarter, not harder. Most modern data tools already support parquet as a format. Platforms like AWS, Azure, and Google Cloud make it easy to export or convert your files with just a few clicks.
If your team uses Python or Spark, converting CSVs to parquet can be done with a few lines of code. You can even test things out on a single data set before rolling it out across the board.
The real win here is that parquet doesn’t require you to reinvent your stack. You can start small by converting large, frequently accessed files first and see an immediate performance bump.
Faster queries. Lighter storage. Easier data sharing.
The bottom line? If your data is growing, your tooling should evolve with it. Parquet is a simple switch with serious upside, and once you see the difference, you won’t want to go back.
Ready to make your data work smarter?
At Network Thinking Solutions, we help businesses stop drowning in bloated file formats and start making smarter, faster moves with their data. Whether you’re scaling up, streamlining workflows, or just trying to get answers faster, we’re here to help you make sense of the tech without the technical headaches.
From optimizing your data pipelines to helping you adopt better formats like parquet, we’re your behind-the-scenes partner in getting it right the first time. No jargon. No hand-holding. Just modern solutions that actually work.
Let’s talk about what’s slowing you down and what we can do about it.