In this post we'll be taking a behind-the-scenes look at cloud storage and how it actually works. Keep reading to learn what an Open Table Format is, why it matters, and the trade-offs you should consider before adopting one.
The Unorganized Library vs. The Catalogued Library
To understand Open Table Formats, picture a massive library. Without an organizing system, thousands of books (data files) are piled into rooms organized loosely by genre or date (folders). If you want to find a specific sentence, you have to walk into every room, open every book, and scan page by page. It's not impossible, but it's tedious and exhausting.
Now, imagine adding a library management system to catalogue the books. Instead of searching through every shelf for a particular book or sentence, you can check the catalog. It tells you exactly which shelf holds the book you need, so you only have to scan its pages for the desired sentence. The catalogue tracks whenever a book gets updated or replaced, and prevents two people from accidentally checking out the same physical book at the same time.
An Open Table Format (OTF) is that digital catalog. It sits on top of cheap cloud object storage (like Amazon S3 or Google Cloud Storage) and acts as a metadata layer—a smart management system that tracks, organizes, and secures raw files so they can be treated as structured database tables.
Key Terms to Know
Before we look at why engineers favor this setup, let's unpack a few technical terms:
- Metadata: data about data. Metadata stores information about files—such as file size, column names, creation dates, and exact storage locations instead of the actual records.
- ACID Transactions: a set of rules (Atomicity, Consistency, Isolation, Durability) that guarantee database reliability. Essentially, if an update fails halfway through, the engine rolls everything back so data never gets left in an unfinished state.
- Time Travel: the ability to query a dataset as it existed at a specific point in the past. Because changes are tracked incrementally in the metadata, you can easily inspect historical versions without keeping full duplicate copies of your data. (This is an important perk of Open Table Formats)
- Schema Evolution: allows you to change the structure of a table—add, delete, or rename a column—without breaking downstream reports or having to rewrite all historical data files. (This is another huge benefit to Open Table Formats)
Why Open Table Formats are a Big Deal
Historically, storing massive amounts of data meant choosing between two extremes:
- Data Lakes: a vast pool of raw data stored in its original, native state on low-cost object storage (for example, JSON files stored as AWS S3 objects). While it is very cheap to store files, storage is messy, prone to corrupted data, and slow to query. Data lakes operate under a Schema-On-Read model, meaning that raw files are dumped into the lake in their native format without defining a blueprint upfront. Structure is only applied later when a query engine reads the data.
- Data Warehouses: an optimized, structured database built specifically for fast SQL queries and business intelligence (BI) reporting. They are fast and reliable, but expensive and often locked into proprietary file formats. Their Schema-On-Write model means that the table structure (columns, data types, keys) must be strictly defined before any data is loaded. If incoming data doesn't fit the blueprint, it gets rejected.
An Open Table Format gives you the best of both worlds. By storing raw files in open standards (like Parquet) and adding a smart metadata catalog layer on top, you get warehouse-like features—like ACID compliance, single-row updates, and time travel—at a fraction of the storage cost. Additionally, because the format is open, you aren't locked into a single software tool. Multiple query engines can read from and write to the exact same table simultaneously without interfering with one another.
Trade-Offs to Consider
As with any architecture choice, Open Table Formats aren't flawless. Here are three key trade-offs to keep in mind:
1. Management Overhead (Compaction):
When you update or delete rows in an Open Table Format, the underlying engine doesn't rewrite entire multi-gigabyte files instantly. Instead, it creates small delta files and updates the metadata catalog. Over time, millions of tiny files pile up, which can slow down queries. You need regular maintenance jobs (known as compaction) to merge small files back into optimized chunks.
2. Metadata Inflation:
The metadata layer itself takes up storage space and processing power. If your tables undergo millions of tiny updates every minute, the metadata history can balloon quickly, making snapshot lookups heavier to compute if not pruned regularly.
3. Setup Complexity:
For small-scale data projects or straightforward visual reporting, introducing an Open Table Format layer might add unnecessary architectural complexity. If a standard relational database or a flat file meets your needs, adding an OTF metadata layer might be unwise.
Data engineering is constantly evolving to make data cleaner, faster, and more accessible. Just as good documentation habits bring clarity to complex workbooks, Open Table Formats bring order to cloud storage—giving us transaction safety, historical tracking, and multi-engine flexibility.