Delta Lake

A Delta Lake is an open-source storage layer designed to run on top of an existing data lake and improve its reliability, security, and performance. Delta Lakes support ACID transactions, scalable metadata, unified streaming, and batch data processing. Delta Lake is the optimized storage layer that provides the foundation for tables in a Lakehouse on Databricks. Delta Lake is open-source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale.

Delta Lake is the default format for all operations on Databricks. Unless otherwise specified, all tables on Databricks are Delta tables. Databricks originally developed the Delta Lake protocol and continues to actively contribute to the open-source project. Many of the optimizations and products in the Databricks platform build upon the guarantees provided by Apache Spark and Delta Lake. For information on optimizations on Databricks

Delta Lake Benefits

A Delta Lake offers your organization many benefits and use cases, such as the following:

Data Integrity and Reliability: It ensures data integrity and reliability during read and write operations through support for ACID transactions (Atomicity, Consistency, Isolation, Durability). This ensures data consistency, even with concurrent writes and failures.

Data Quality and Consistency: It maintains data quality and consistency by enforcing a schema on write.

Auditability and Reproducibility: It supports version control and time travel. This means that it enables querying data as of a specific version or time, facilitating auditing, rollbacks, and reproducibility, supported by its versioning feature.

Operational Efficiency: It seamlessly integrates batch and streaming data processing, providing a unified platform for both, supported by its compatibility with Structured Streaming.

Performance and Scalability: It effectively manages metadata for large-scale data lakes, optimizing operations such as reading, writing, updating, and merging data. It achieves this through techniques like compaction, caching, indexing, and partitioning. Additionally, it leverages the power of Spark and other query engines to process big data efficiently at scale, improving data processing speeds.

Flexibility and Compatibility: Databricks Delta Lake preserves the flexibility and openness of data lakes, allowing users to store and analyze any type of data, from structured to unstructured, using any tool or framework of their choice.

Secure Data Sharing: Delta Sharing is an open protocol for secure data sharing across organizations.

Security and compliance: It ensures the security and compliance of data lake solutions with features such as encryption, authentication, authorization, auditing, and data governance. It also supports various industry standards and regulations, such as GDPR and CCPA.

Open-Source Adoption: It’s completely backed by an active open-source community of contributors and adopters.

Delta Lake Architecture

As stated above, a Delta Lake is an open-source storage layer that provides the foundation for tables in a data Lakehouse on Databricks. It uses a transaction log to track changes to Parquet data files stored in cloud object stores such as Azure or S3. This supports features such as unifying streaming and batch workloads, versioning, snapshots, and scalable metadata handling.

This diagram shows how a Delta Lake uses APIs to integrate with your existing storage systems such as AWS S3 and Azure, compute engines including Spark and Hive, and APIs for Scala, Java, Rust, Ruby, and Python to make streaming and batch data available for analytics and machine learning:

The Bronze layer captures raw data “as-is” from external systems, the silver layer refines it, and the gold layer represents valuable insights and knowledge. Let’s dig a bit deeper:

• Bronze Layer (Raw Data): serves as the initial landing zone for data ingested from various sources. It contains raw, unprocessed data, including any additional metadata columns (such as load date/time or process ID). The focus here is on quick Change Data Capture and maintaining an historical archive of source data.
• Silver Layer (Cleansed and Conformed Data): refines the data from the bronze layer to provide a more structured and reliable version that serves as a source for analysts, engineers, and data scientists to create projects and analyses. Typically, minimal transformations are applied during data loading into the silver layer, prioritizing speed and agility. Still, it can involve matching, merging, conforming, and cleansing the data to create an “Enterprise view” of key business entities and transactions. This enables self-service analytics, ad-hoc reporting, advanced analytics, and machine learning.
• Gold Layer (Aggregated and Knowledge-Ready Data): represents highly refined and aggregated data. It contains information that powers analytics, machine learning, and production applications. Unlike Silver, Gold tables hold data transformed into knowledge, rather than just information. Data in the Gold layer undergoes stringent testing and final purification. It’s ready for consumption by ML algorithms and other critical processes.

Here are the keyways the Databricks Delta architecture differs from a typical data lake architecture:

Databricks Delta Table is like a massive spreadsheet designed for large-scale analysis. It organizes data in a clean, columnar format, ensuring rapid querying. Unlike conventional tables, Delta Lake tables are transactional, meticulously recording every change. This robust approach maintains data consistency even as schemas change over time.

In a Delta Lake table, data changes (insertions or modifications) are initially stored as JSON files in cloud storage. These files are then referenced and added to the Delta log. The loosely coupled architecture of Delta enables efficient metadata handling and scalability.

Delta Logs serve as both the System-of-Record and Source-of-truth for the table, keeping track of all transactions. All queries utilize the Delta log, which maintains the complete history of changes. Imagine them as a digital ledger that precisely documents every modification made to tables. Regardless of the scale or diversity of changes, these logs ensure data integrity by capturing every alteration. This functionality supports features such as point-in-time queries, rollbacks, and auditability in case any issues arise.

The Cloud Object Storage Layer plays a crucial role in a Delta Lake, responsible for storing data. It seamlessly integrates with various object storage systems such as HDFS, S3, or Azure Data Lake. This layer ensures data durability and scalability, allowing users to store and process extensive datasets without dealing with the complexities of managing the underlying infrastructure.