If you’ve worked in data engineering or analytics, chances are you’ve come across Apache Parquet. It’s a file format built for big data, known for its efficiency and scalability. Parquet is everywhere: cloud storage, data lakes, and even modern analytics pipelines. But here’s the question: why hasn’t anyone created a BI tool specifically designed to work with Parquet files?

Let’s break it down, exploring what Parquet is, why it’s such a big deal, and what’s missing when it comes to tools.

What Exactly Is Apache Parquet?

If you’re new to Parquet, think of it as a data format optimized for analytics. Unlike a traditional spreadsheet or a database table that stores data row-by-row, Parquet stores data column-by-column. Why does that matter? Here are a few reasons:

1. Compression. Parquet groups similar data types together in columns, making it easier to compress. This saves a ton of storage space.

2. Selective Reading. You can pull just the columns you need instead of scanning the entire dataset, which speeds up queries.

3. Self-Describing. Each Parquet file includes schema and metadata, so you don’t need an external definition to understand the data.

Parquet is powerful for machines to process efficiently, but if you’ve ever tried opening a Parquet file to inspect its contents, you know it’s not exactly user-friendly.

Why Would You Want to Visualize Parquet Files?

If Parquet is built for machines, why would humans need to visualize it? Turns out, there are some good reasons.

1. Exploring Data at Scale

When you’re working with datasets stored in Parquet, it’s not always obvious what’s inside. Visualization tools can help you:

• Get a quick look at the schema and data types.

• Spot patterns in the data, like trends or outliers.

• Double-check your work during ETL pipelines.

2. Debugging Data Pipelines

Modern data pipelines are complex, with multiple stages of filtering, joining, and aggregating. If something goes wrong, say, a schema mismatch or unexpected null values.. it’s useful to inspect the intermediate datasets stored in Parquet. Visualization can help answer questions like:

• Did my filters actually work?

• Are the joins producing duplicates or missing rows?

• How did the data transform at this step?

3. Optimizing Performance

If you’re working with Parquet data directly, understanding column cardinality, file sizes, or compression levels can help you fine-tune both storage and queries. For example:

• Should you use dictionary encoding for this column?

• Is a column with high cardinality slowing down your queries?

• Are file sizes too small, causing overhead for distributed processing?

4. Making Data More Accessible

Parquet is a technical format, which often means that today only engineers or data scientists interact with it directly. But as more teams adopt cloud-native data lakes, making Parquet data accessible to non-technical team members becomes important. A visualization tool could bridge this gap.

What’s Changing in Parquet Workflows?

Parquet has traditionally been used for backend batch processing, but things are evolving. New trends in data workflows are making it more important to interact with Parquet in real-time and at different stages of the pipeline.

1. Real-Time Pipelines

Tools like Kafka have made it possible to stream data into Parquet. That means Parquet isn’t just for batch jobs anymore. It’s showing up in real-time workflows where immediate feedback is way more important.

2. Cloud-Native Data Lakes

Parquet is the default file format for many cloud-based data lakes. Services like Athena, BigQuery let you query Parquet files directly. But while these services handle querying well, they aren’t designed for exploration or visualization.

3. Better Tooling

Libraries like DuckDB and Apache Arrow make it easier to query Parquet files locally or in-memory. Meanwhile, Python tools like Pandas and PyArrow allow developers to programmatically interact with Parquet data. These tools are helpful, but they still don’t provide the kind of visual, interactive experience you’d get from a BI tool.

What Could a Parquet-Focused BI Tool Look Like?

Traditional BI tools often require you to load data into a relational database or a warehouse before you can analyze it. That’s fine for some workflows, but it adds overhead, especially when your data is already sitting in Parquet. A purpose-built BI tool for Parquet could cut out the middleman and work directly with the files.

1. Direct Querying. Query Parquet files right from your cloud storage (e.g., S3, Azure Blob, or Google Cloud Storage) or local drives. No need to move data into another system.

2. Schema Exploration. Visualize the file structure, including nested columns and metadata. This could make it easier to understand the data before diving in.

3. Interactive Dashboards. Create simple dashboards or charts directly from Parquet data. This would be especially helpful for lightweight, ad-hoc analysis.

Who Would Use It?

A Parquet-specific BI tool might not replace traditional BI platforms, but it could be useful for:

• Data Engineers debugging pipelines, validating transformations, and optimizing performance.

• Data Analysts running ad-hoc analysis on datasets stored in data lakes.

• Startups/SMBs avoiding the cost and complexity of data warehouses.

Why Now?

The way we use Parquet is evolving. What used to be a backend storage format is now playing a bigger role in real-time analytics and interactive workflows. Teams need faster, easier ways to work directly with their data, and the current tools don’t fully address that.

Try Preswald today!

https://github.com/StructuredLabs/preswald