Delta Lake :The Time Traveller's Data Guide

I often find myself navigating the choppy waters of big data, helping folks keep their data clean, consistent, and orderly. Today, I want to talk about something on Data, Delta Lake, a pattern that my recent project ‘Asset AI’ relied upon ( a great project, with a great team making it memorable).

So, let’s get into it and I promise it won’t be boring! so continue reading on.

Delta Lake, why is it called Delta Lake?

You might be thinking it’s named after a tranquil body of water nestled in a serene delta where rivers meet the sea. Nope, it’s more exciting than that (I guess)! In the world of data, "Delta" signifies change or difference, and Delta Lake is all about capturing these changes, much like a time traveler keeping a diary of all the eras they’ve visited.

Delta Lake was invented by Databricks, by the creators of Apache Spark. The project was open-sourced in 2019, and it has since grown with contributions from multiple organisations such as Microsoft and Intel.

Data Lake -> Delta Lake, who is who in this relationship?

Delta Lake can be thought of as an enhancement or extension of a traditional data lake. Imagine a data lake as a sprawling, diverse library filled with books, magazines, and articles from all over the world, where anyone can browse and deposit information. While this library is vast, finding a specific book quickly or ensuring all volumes are properly organized can be challenging.

In comes Delta Lake, akin to an intelligent library catalog and librarian rolled into one. It tracks each book's changes and revisions, ensures data consistency and accuracy, and provides a seamless borrowing experience. With Delta Lake's ACID transactions, versioning, and schema enforcement, it's like having a catalog that precisely indexes every book and lets you borrow or return volumes without error. You can even see earlier editions if needed.

The Diary of Incremental Changes

Imagine you could remember every change you've ever made to your wardrobe. That regrettable "all-denim" phase in the '90s, followed by the low-rise jeans craze of the 2000s—every bit of it documented. That’s what Delta Lake does with your data. It tracks incremental changes, ensuring you can always revisit and understand the historical changes. It's like having a fashion diary, but for data.

The Transaction Log: The river of knowledge, capturing all the streams!

Delta Lake maintains a meticulous transaction log that captures every change in the data lake. Think of it as the gossip columnist of the data world, detailing who did what and when. This log isn’t just about keeping records; it’s foundational for magical features like rollbacks (undoing your data faux pas) and full data audits.

Merge, Update, Delete—The Data Puzzle

Handling data in Delta Lake is like piecing together a complex puzzle. It carefully manages merges, updates, and deletes—or "upserts”. These operations refine existing data, shifting pieces to fit perfectly into the larger picture. It’s the intricate assembly of data, continuously reworked for a flawless composition.

Schema Enforcement Meets Evolution

Delta Lake is like the strict librarian who insists on a specific way to arrange books but suddenly decides it’s okay if some books want to change their genre. It enforces a schema when writing data but allows for the schema to evolve. Managing schema deltas means your data can grow and change.

Time Travel—Yes, Really

With Delta Lake, you can actually "time travel." No, it’s not fiction! You can query earlier versions of your data, giving you the power to revisit the past states of your information as if you’re a data historian. Made a mistake? Just hop back in time and fetch the original data.

Performance Comparison

Think of Delta Lake like the turbo button on an old-school video game console. It doesn’t just store your data; it supercharges your data processing. Delta Lake optimises file management and querying, which significantly boosts performance, especially in big data scenarios where traditional data lakes might lag like a glitchy game level. It's like comparing a sleek sports car to a sturdy sedan—both get you where you need to go, but Delta Lake does it with a flair for speed and efficiency.

Challenges with Delta Lake

Flexibility in Adding Data

One notable challenge with Delta Lake is its strict schema enforcement, which can be a double-edged sword. While it ensures data quality and consistency, it also means that adding new data types or unexpected schema changes can be cumbersome. You can't just throw new data into the lake and expect it to swim; it needs to be carefully introduced to match the existing schema, potentially slowing down flexibility in data ingestion.

Unrestricted Data Exploration

Delta Lake's structured approach to data management may restrict the ability to freely explore data for new use cases. The transaction log and schema validation are excellent for maintaining integrity but can put a damper on the wild, exploratory data analysis that data scientists love. It's like having a guided tour in a museum—you see the highlights, but you might miss wandering into an intriguing, hidden corner.

One Solution: The Medallion Architecture

The Medallion architecture addresses both challenges by organising data into different tiers—Raw, Bronze, Silver, and Gold. This tiered approach allows for the gradual integration of new data types at the Raw level without affecting downstream processes, thereby enhancing flexibility in data ingestion. Additionally, it supports unrestricted data exploration by allowing data scientists to work with data in the Raw and Bronze layers, which are closer to their original form and less governed by stringent schema rules. This separation of layers enables exploratory analysis and the prototyping of new use cases without compromising the integrity of more curated data in the Silver and Gold layers. As insights solidify and use cases become defined, data can then be promoted to these higher layers, where it benefits from the full governance and schema enforcement that Delta Lake offers. This strategy effectively balances rigorous data management with the flexibility needed for innovation and exploratory data science.

Time for conclusion

This exploration into the many facets of Delta Lake, from managing incremental changes to time travel capabilities, is just one aspect of what it offers. Delta Lake comes with a plethora of additional benefits that make it indispensable in the world of data management. For instance, it prevents data corruption through ACID transactions, enables faster queries by optimizing file management, and increases data freshness with seamless batch and streaming integration. Additionally, it helps reproduce machine learning models with consistent data pipelines and allows organisations to achieve compliance through comprehensive audit logging and security.