What Is a Parquet File? Key Things to Know

File format is one of the biggest bottlenecks in data analytics. Text-based formats such as CSV and JSON are easy to read and share, but they weren't designed for the scale and complexity of modern analytical workloads. Every query pays a performance penalty because these formats don't distinguish between the data you need and the data you don't.

Parquet solves this problem. It's a file format built specifically for analytical workloads in data engineering, data science, and big data systems. Storing data column by column instead of row by row allows queries to read only what they need. And what is the result? Faster queries, lower storage costs, and less wasted compute.

In this article, I'll walk you through what makes Parquet different, how it works at a high level, and when you should use it. I'm focusing on concepts and use cases rather than low-level implementation details or code. If you want to get hands-on with code examples and practical implementation, we have a detailed guide on Apache Parquet for data professionals that covers the technical side.

What Is a Parquet File?

So what exactly is Parquet? At its core, it's a columnar storage file format built for efficient data storage and fast analytical queries. Instead of storing data row by row, as in traditional formats, Parquet stores it column by column. This seemingly simple change makes it well-suited for large-scale data processing.

Parquet works best in scenarios where you:

• Read only a subset of columns from a dataset
• Scan large volumes of data
• Perform aggregations, filtering, and analytical queries

The format is part of the Apache ecosystem and maintained as an open-source standard by Apache Parquet. Because it's open and well-supported, Parquet integrates smoothly with many modern data tools, including data warehouses, data lakes, and distributed processing frameworks.

Columnar storage vs. row-based formats

Row-based formats (CSV, JSON) store complete records in a single row. This works well for transactional use cases or when you need to read entire rows at once.

Columnar formats (Parquet) store all values from the same column together. This lets analytics engines read only the columns they need, skipping everything else.

Here's an example: if a dataset has 50 columns but your analysis only needs 3, a columnar format like Parquet can read just those 3. With a row-based format, the entire row must be scanned, even if most of the data goes unused.

Let's make this concrete. Imagine you're analyzing e-commerce transactions. Your dataset contains 40 columns, but you only need to calculate the average order value by month. You only need order_total and order_date. With Parquet, your query reads exactly those 2 columns. With CSV, it reads all 40 columns for every single row, even though 38 of them are irrelevant to your analysis. On a dataset with millions of transactions, that difference is massive.

This kind of efficiency is fundamental to how data becomes useful. If you're interested in how raw data progresses to actionable insights, our data-information-knowledge-wisdom pyramid cheat sheet breaks down this transformation.

Why Parquet is widely used

This columnar design translates into several practical advantages for analytics:

• Better compression, since similar data types stored together compress more effectively
• Faster query performance, as analytics engines process less data overall
• Lower storage costs compared to plain-text formats

These properties make Parquet a common choice in data lakes, cloud storage systems, and analytics pipelines where efficiency matters more than human readability.

In short, Parquet is a column-oriented, open-standard file format built to handle large datasets efficiently, especially when the goal is analysis rather than simple data exchange.

Why Parquet Files Are Used in Data Analytics

Now that we've covered what Parquet is, let's look at why it's become so popular in data analytics. The reason isn't that Parquet is easier to work with than other formats. It's that Parquet is far more efficient at scale, and most analytical workloads aren't like transactional systems, where you fetch one complete record at a time. Instead, they scan large datasets and focus on a subset of columns.

Columnar storage fits this pattern naturally. When data is stored column by column, analytics engines read only the fields they need. If a query touches five columns out of fifty, Parquet lets the engine skip the remaining forty-five entirely. This dramatically reduces disk I/O, which is often the main bottleneck in data processing.

Compression is another major advantage. Values in the same column usually share similar data types and ranges, so Parquet compresses them far more effectively than row-based formats. Better compression means smaller files, lower storage costs, and less data to transfer during query execution.

These two factors (reduced I/O and improved compression) translate directly into faster queries. This is why Parquet has become a default choice in many analytics engines and distributed query systems.

Parquet is now a standard format in data lakes and cloud analytics platforms, where large volumes of data are stored once and queried repeatedly. In those environments, performance and cost efficiency matter more than human readability, making Parquet a better fit than text-based formats like CSV or JSON.

If you're working with Python, you'll often need to move between different file formats. Our guide on importing data in Python covers how to work with Parquet alongside CSV, JSON, and other formats.

How Parquet Files Store Data (High-Level Overview)

We've talked about the benefits of columnar storage, but how does Parquet actually organize data on disk? Instead of writing complete records sequentially (row-based), Parquet groups all values from each column together.

In a row-based format, each record is written in full before moving on to the next one. This works well when you frequently need entire rows, but it becomes inefficient when analytical queries only care about a few fields.

Parquet takes the opposite approach. All values from the same column are stored together, and each column is independent. When a query runs, the analytics engine scans only the relevant columns and ignores the rest. If a query applies conditions to a specific column, the engine evaluates those conditions directly without touching unrelated fields. Fewer disk reads, faster execution.

You don't need to understand Parquet's internal structures to benefit from it. The key idea is simple: storing similar data together enables smarter reads. That single design choice makes Parquet effective for analytical workloads, even before considering compression or other optimizations.

These storage patterns become especially important when you're working with large-scale data systems. If you're using PostgreSQL for structured queries, check out our PostgreSQL basics cheat sheet for query optimization tips. And if you're dealing with truly massive datasets, you might want to explore data partitioning strategies to combine with Parquet's columnar storage.

This column-oriented layout explains why Parquet performs so well in analytics-intensive environments and why it has become a foundational format in modern data systems.

Key Features of the Parquet File Format

Beyond the core concept of columnar storage, Parquet includes several features that make it especially effective for analytical workloads. Rather than optimizing for human readability, the format prioritizes efficient storage, fast querying, and interoperability across modern data tools.

Columnar storage

Parquet stores data by columns instead of rows, allowing analytics engines to read only the fields needed for a query. This reduces unnecessary data scans and significantly improves performance for large datasets.

Efficient compression

Because values in the same column are often similar, Parquet achieves much higher compression ratios than row-based formats. Smaller file sizes mean lower storage costs and faster data transfers.

Schema support

Parquet files include an explicit schema that defines data types and structure. This ensures consistent data interpretation across tools and prevents common issues caused by loosely typed text formats. If you're working with Power BI, schema management is especially important. You can learn more about handling table structures in our guide on working with tables in Power Query M.

Built-in metadata for faster queries

Parquet stores metadata about columns and data blocks, which allows query engines to skip irrelevant sections of a file. This makes filtering and selective reads more efficient without scanning the entire dataset.

Compatibility with big data tools

Parquet is supported by most modern data processing frameworks and query engines, making it a reliable choice for data lakes and cloud analytics workflows. This broad compatibility helps teams avoid format lock-in while maintaining performance.

The format works smoothly across different tools and languages. If you're building dashboards, our Power BI Fundamentals track covers how to work with various data sources efficiently. For R users doing large-scale data manipulation, the data.table package pairs well with Parquet for high-performance analytics.

Parquet vs. CSV and Other File Formats

At this point, you might be wondering how Parquet stacks up against formats you're already familiar with, like CSV. The short answer is that Parquet and CSV are designed for different use cases.

CSV is a row-based, plain-text format. It's easy to create, easy to open in a text editor or spreadsheet, and simple to share. That makes it a good choice for small datasets, quick exports, and basic data exchange between systems or people.

Parquet, by contrast, is a binary, columnar format built for analytics. Instead of optimizing for readability, it optimizes for performance. Analytical queries often need only a few columns from a large dataset, and Parquet is designed to read just those columns efficiently. This makes large-scale data analysis much faster and more cost-effective, especially in distributed environments.

That same design also explains Parquet's trade-offs. It's not human-readable, and it's rarely the right choice for ad hoc data sharing or manual inspection. For those use cases, formats like CSV or JSON remain more practical.

Other formats sit somewhere in between. JSON is flexible and widely used for APIs, but it's inefficient for analytics at scale. Avro and ORC, like Parquet, are designed for big data systems, but they serve slightly different roles depending on whether row-based or column-based access is preferred. If you're trying to decide between these formats, we've put together a detailed comparison of Avro vs. Parquet that walks through the trade-offs.

In practice, Parquet has become the most common choice when query performance and storage efficiency matter most.

Where Parquet Files Are Commonly Used

Given everything we've covered about how Parquet works and why it's effective, let's look at where you'll actually encounter it. Parquet files are most common in environments where large volumes of data are queried repeatedly for analysis, rather than read once from start to finish.

Data lakes and lakehouse architectures

One common use case is data lakes and lakehouse architectures, where raw and processed data is stored cheaply and queried on demand. Parquet's efficient storage and selective column reads make it well-suited for these large, evolving datasets.

Business intelligence and analytics workloads

Parquet is also widely used in business intelligence and analytics workloads. Dashboards, reports, and exploratory analysis often scan only a subset of columns across many records, which aligns perfectly with Parquet's columnar design.

Tools such as Power BI and Tableau often use Parquet to improve performance. If you're working in Power BI and want to get better at managing data assets, our course on deploying and maintaining assets in Power BI covers best practices. And if you're preparing to validate your Power BI skills professionally, check out our guide on passing the PL-300 Power BI certification.

Machine learning workflows

In machine learning workflows, Parquet is frequently used to store features and training data. Models can load only the features they need, reducing I/O overhead and speeding up experiments on large datasets. When you're doing exploratory data analysis on these feature sets, visualization libraries like Plotly Express work well with Parquet. Our Plotly Express cheat sheet shows you how to create interactive visualizations efficiently.

Cloud-based and distributed query systems

Finally, Parquet is well-suited to cloud-based and distributed query systems, where performance and cost are closely tied to the amount of data scanned. By minimizing unnecessary reads and compressing data effectively, Parquet helps these systems scale efficiently as data volumes grow.

Tools and Platforms That Support Parquet Files

Knowing where Parquet is used is one thing, but what about the actual tools you'll use to work with it? One reason Parquet has become so widely adopted is broad support across modern data ecosystems. You're not learning a niche or vendor-specific format. Parquet is a de facto standard in large-scale analytics.

Most distributed query engines and data processing frameworks can read and write Parquet natively. It's commonly used in cloud data platforms, big data processing systems, and analytics engines designed to efficiently scan large datasets. Because Parquet is an open, Apache-backed format, it integrates cleanly across tools without locking users into a single vendor or stack.

Parquet is also well supported in data science and machine learning workflows, where datasets must be shared across teams, pipelines, and environments. The same Parquet files can often be used for exploration, reporting, and model training without conversion. If you're working with R and need reproducible research documents, Quarto works well with Parquet to create reports that blend code, analysis, and results.

This wide compatibility is part of Parquet's appeal. Choosing Parquet doesn't mean committing to a specific database, cloud provider, or analytics engine. It means adopting a format that works across platforms and scales with your data architecture.

Limitations and Trade-offs of Parquet Files

With all these advantages, you might think Parquet is the answer to every data storage problem. It's not. Despite its strengths, Parquet isn't a universal solution. Its design favors certain workloads, and understanding its limitations helps avoid misuse.

Not optimized for frequent updates

Parquet is optimized for read-heavy analytics, not frequent updates or small, incremental writes. Writing data to Parquet typically happens in batches, which makes it a poor fit for transactional systems or real-time record-by-record updates.

Schema management complexity

Schema management can also be more complex than with simple text formats. While Parquet supports schemas and evolution, changing column definitions over time requires coordination and care, especially in shared data environments.

Not a database replacement

Parquet isn't designed to replace databases for operational workloads. It lacks native support for transactions, point-lookup indexing, and low-latency updates—all of which are critical for application-facing systems.

The small files problem

Finally, Parquet can suffer from what's often called the "small files problem." Storing data as many tiny Parquet files reduces the efficiency gains of columnar storage and can hurt performance in distributed systems. Parquet works best when data is written in reasonably sized chunks that align with how it will be queried.

These trade-offs don't diminish Parquet's value, but they do clarify where it fits best: large-scale, read-optimized analytics rather than transactional or highly mutable workloads.

Common mistakes to avoid with Parquet

Creating too many small files is one mistake. This is because writing thousands of tiny Parquet files defeats the purpose. Aim for files that are at least tens of megabytes, ideally hundreds. In distributed systems, this usually means configuring your write operations to batch appropriately.

Using Parquet for frequently updated data is another mistake. If you're updating individual records throughout the day, Parquet isn't the right choice. It's designed for batch writes, not incremental updates.

Ignoring schema evolution is a mistake. When you need to add or change columns, plan ahead. Parquet supports schema evolution, but it requires careful coordination, especially in shared data environments.

Finally, not partitioning large datasets would be a mistake. For very large datasets, you should consider partitioning by commonly filtered columns. This lets query engines skip entire files that don't match your filter criteria.

When You Should (and Shouldn't) Use Parquet

So with all these considerations in mind, when should you actually use Parquet? The answer comes down to matching the format to your workload. Parquet is a strong choice, but only when it matches the kind of work you're doing. Thinking about how your data is read and written usually makes the decision straightforward.

When Parquet is a good choice

• You're working with analytical or reporting workloads that scan large datasets
• Queries typically read only a subset of columns, not entire rows
• Data is written in batches—daily ingests or scheduled pipelines, for example
• Storage efficiency and query performance matter more than human readability
• You're building or contributing to a data lake or lakehouse architecture

In these situations, Parquet's columnar layout, compression, and metadata pay off quickly.

When simpler formats may be better

• You need files that are easy to inspect or edit by hand
• Data is exchanged between systems in small volumes or ad hoc workflows
• You're dealing with frequent updates or transactional writes
• Simplicity and interoperability matter more than performance

Formats like CSV or JSON are often a better fit for lightweight data exchange or early-stage workflows. Think of them like configuration files in your development environment (if you've ever worked with dotfiles, you know why plain text matters for files you need to read and edit frequently).

The key idea is that Parquet shines in read-heavy, analytical environments. When your workload fits that pattern, it's hard to beat. When it doesn't, simpler formats often lead to fewer problems.

Conclusion

Here's the practical takeaway: if you're working with large datasets and your queries typically need only a subset of columns, Parquet will likely save you time and money. If you're doing frequent updates on individual records or need human-readable files, stick with simpler formats.

The beauty of Parquet is that it's an open standard with broad ecosystem support. You can use it across tools without locking into a single vendor. Whether you're an analyst running queries, an engineer building pipelines, or an architect designing data systems, Parquet fits naturally into modern, distributed environments.

Start small if you're new to Parquet. Try converting one of your frequently queried CSV files to Parquet and compare query performance. The difference on large datasets is usually obvious. From there, you can decide where else columnar storage makes sense in your workflow.

Want to dive deeper into implementation? Our Apache Parquet tutorial walks through practical examples with code, and we have plenty of other resources on modern data formats and analytics tools to help you build more efficient data systems.