Delta Lake

What it is, benefits, and how it works. This guide provides practical advice to help you understand and adopt delta lake for a reliable data lake solution.

What is Delta Lake?

Delta Lake is an open-source storage layer that uses the ACID compliance of transactional databases to bring reliability, performance, and flexibility to data lakes. It is ideal for scenarios where you need transactional capabilities and schema enforcement within your data lake.

A Delta Lake enables you to build Data Lakehouses, which support data warehousing and machine learning directly on the data lake. And with features like scalable metadata handling, data versioning, and schema enforcement for large-scale datasets, it ensures data quality and reliability for your analytics and data science tasks.

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 key ways 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.

Cloud Data Lake Comparison Guide

Get an unbiased, side-by-side look at all the major cloud data lake vendors, including AWS, Azure, Google, Cloudera, Databricks, and Snowflake.


What is Databricks?

Databricks is a unified, open analytics platform designed for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. It integrates with cloud storage and security, manages cloud infrastructure, and provides tools for data processing, visualization, machine learning, and more. The platform was founded by the original creators of Apache Spark and offers an efficient workspace for data scientists, engineers, and business analysts to collaborate on data-driven applications.

Delta Lake in Databricks

Delta Lake serves as the optimized storage layer for tables in a Databricks lakehouse. By default, all Databricks operations use it as the storage format. So, unless you explicitly specify, every table on Databricks is a Delta table. Databricks was the original developer of the protocol and remains an active contributor to the open source project. The Databricks platform leverages the assurances offered by Apache Spark and Delta Lake for its optimizations and products.

Managed Data Lake Creation

Deploying a data lake strategy presents your data engineering team challenges due to traditional data integration processes relying on slow, hand-coded SQL or Python scripts and scarce engineering resources. However, adopting a fully automated approach for end-to-end data lake creation can significantly accelerate your ROI.

Managed data lake creation automates the entire data pipeline, covering real-time ingestion, data processing, and refining raw data for consumer access. The best solutions offer the following features:

  • Easy construction and management of agile data pipelines supporting major data sources and cloud targets, all without code.

  • Zero-footprint change data capture technology for real-time data delivery without disrupting production systems.

  • Comprehensive lineage tracking to ensure data trust.

  • A secure, enterprise-scale data catalog that empowers data consumers to search, understand, and access curated data.

3 Ways to Increase Your Data Lake ROI

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