What is Delta Lake? Benefits, features, and architecture

It doesn't take much for your data lake to end up looking more like a data swamp, whether from inconsistent cataloging, ad hoc integrations, or other forms of poor organization and planning.

Delta Lake clears up the murky waters by adding a layer of structure and reliability to your data lakes. 

In this guide, we discuss what Delta Lake is and how it ensures transactional consistency and enforces compliance within lake systems. 

What is Delta Lake?

Delta Lake is an open-source storage layer — an open table format — that sits on top of existing data lakes. Whether you’re running AWS S3, Azure Data Lake Storage, or another data lake tool, Delta Lake brings atomicity, consistency, isolation, and durability (ACID) transactions to your workloads. 

Developed in 2016 by Databricks, Delta Lake combines Apache Parquet data files with a more robust metadata management system. It comes with a transaction log (DeltaLog) that records any changes to a table, providing access to consistent, versioned metadata for more reliable reads and writes. This means you can optimize query performance and safely handle concurrent interactions without corrupting data. 

By transforming messy raw data into a structured asset that you can use for analytics at scale, Delta Lake brings much-needed order to your existing data lakes.

Delta Lake vs. data lakes: Key differences

Data lakes are low-cost storage solutions that excel at holding raw data. You can direct all your incoming content to a lake and then process or structure it later. However, it’s also difficult to enforce compliance rules, leading to low protection against corruption, partial writes, or schema drift. 

Delta Lake fixes these issues through additional features, such as DeltaLog, which tracks every performed transaction. Delta Lake also lets you apply schemas and edit content safely without any merging issues. 

If data lakes are where data is stored, Delta Lake is how it’s stored — a tabular, controllable, and compliance-first layer.

Delta Lake features

Delta Lake comes with a range of features that traditional data lakes lack, including:

• Atomicity: Rather than filling your storage with part-finished tables, atomicity means every write operation is all-or-nothing. If progress is interrupted, the entire process is reset, meaning less damaged data and higher content quality.
• Consistency: Every transaction follows the rules you’ve set out, even when you’re running multiple workloads at the same time. At scale, this prevents quality issues or data from drifting into invalid states. You’ll also benefit from everyone always working from the same set of data.
• Open table format: Delta Lake typically stores data in open-source Parquet files. This means that whichever storage tools you use, you can always easily embed a Delta Lake layer. You’ll never run into vendor lock-in or have to replatform your data.
• Schema enforcement: By enforcing a strict tabular schema, Delta Lake prevents schema drift. If incoming data doesn’t meet your policy requirements, the write won’t go ahead.

How Delta Lake works

Delta Lake’s architecture is made up of two fully independent components: object storage through data files and the transaction log.

Data files use the Parquet format and are stored as “Delta tables”. The transaction log maintains metadata alongside your object storage and tracks any changes to your files, including when and by whom. DeltaLog also enables features like ACID transactions and versioning. Whenever you read from a Delta table, you’re looking at a consistent snapshot of that data at a specific point in time.

Object storage allows you to store huge volumes of data and DeltaLog keeps things consistent and tracked. The result is a system that operationally behaves like a database but has all the scalability you’d expect of a data lake. And, since Delta Lake is open-source, you can integrate it into your storage system without worrying about vendor lock-in.

Delta Lake benefits

Delta Lake helps keep the waters of your data lakes clear. Here are a few of the most notable benefits:

• ACID transactions: Delta Lake ensures that every write follows the ACID principles. Avoiding partial writes allows you to protect against failed jobs, so concurrent workloads can run safely. 
• Scalable metadata handling: Since it’s independent from object storage, DeltaLog doesn’t impact performance as your table sizes or volume of stored data grow.
• Schema enforcement: Incoming data must meet the requirements of a defined schema before it’s written to your Delta Lake system. This additional enforcement stops any inaccurate or malformed data from entering your system.

Delta Lake best practices

To get the most out of Delta Lake, follow these best practices:

• Partition Delta tables: Separating data into columns that you often use in filters will reduce the total amount of content that queries need to scan through, boosting performance. That said, be careful not to over-partition, as each split increases metadata overhead, which can slow reads and writes. 
• Design a scalable layout: Keep a consistent file size standard and minimize the number of stored small files to decrease the scope of your metadata layer and improve performance. 
• Employ Delta Lake-based metadata control: Take advantage of DeltaLog to reduce the need for manual metadata logging.
• Use Z-ordering to accelerate queries: Z-ordering organizes related data within files based on what you filter most often, improving query performance without the need to change partitioning structures. When queries have less data to scan, analytics on larger tables will be much faster.

Enhance your pipelines with Fivetran extensibility

Delta Lake can greatly improve reliability and governance within your data lakehouse. But high-quality content just doesn’t magically appear in your warehouses and lakes — Data pipelines validate, orchestrate, and integrate data at scale while maintaining accuracy and consistency.

The Fivetran Managed Data Lake Service (FMDLS) lets you easily integrate data in Delta Lake format into your destination of choice. FMDLS offers change data capture, normalization, compaction, and deduplication, and absorbs the cost of data ingestion, radically reducing the engineering and financial overhead of using a data lake as a central data repository.

To see just how beneficial Fivetran’s end-to-end pipelines can be, request a live demo today. 

FAQs

What’s the architecture of Delta Lake?

Delta Lake’s architecture diagram is made up of two main components: object storage and a transaction log. Delta tables use Parquet files to store files, while DeltaLog records all changes made to tables. Together, these two processes enable ACID transactions, scalable metadata handling, and versioning across your content.

What’s the difference between Delta Lake and Databricks?

As an open-source storage framework, Delta Lake formats your data lakes through an additional architectural layer. Databricks is the third-party platform that manages Delta Lake.

Where can I find documentation on Delta Lake?

Delta Lake’s documentation is available online and provides information on how to use advanced features.

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