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In this modern era, data has become a game-changer. It helps organizations make critical and informed business decisions.

While many companies collect data through various sources, the most challenging part, the differentiator, is to store and manage the data effectively. Given that the escalating volume of data is becoming increasingly challenging to handle, there is a notable shift from traditional databases towards more contemporary architectural layers, including cloud-based solutions, data lakes, and delta lakes.

What is Delta Lake in Databricks?

Delta Lake is an open-source storage framework that stores data and tables in Parquet files. Delta Lake is compatible with Apache Spark, which supports batch and stream data processing. Azure Databricks uses Delta Lake as its default storage and refers to its tables as Delta tables.

The underlying storage layer for Databricks Delta Lake can be AWS S3, GCP GCS, or Azure BLOB. This article discusses Databricks Delta Lake implementation in Azure and shares best practices to optimize the reads and writes in Delta Lake for better efficiency.

Benefits of Using Delta Lake

The Databricks Delta Lake implementation has many benefits. It is built on a hybrid model that embraces data warehousing and Spark multi-processing principles.

A typical implementation would provide support for:

  • ACID transactions: Delta Lake supports ACID transactions (Atomicity, Consistency, Isolation, and Durability), which provides consistent data and transactional integrity.
  • Scalable: All metadata operations in Delta Lake use an Apache Spark compute engine where data is scalable. This ensures Delta Lake can handle enormous amounts of data without a performance bottleneck.
  • Security and Governance: Delta Lake provides secure and reliable storage of data, which has fine-grained access control for all underlying data at various levels viz, table, row, and column. Security is easy to implement and protects sensitive data from unauthorized access.
  • Time Travel: Imagine if your business suffered from data corruption or incorrect data updated to a sizeable transactional table. In a traditional database, restoring integrity can cost a substantial amount of time and money. However, Delta Lake provides a version history feature that helps prevent data corruption. The table data can roll back to previous versions by running a simple command in Databricks Notebook.
  • Data Sharing: Delta tables can be easily and securely shared with other users. It is platform-independent and supports recipients from outside your organization as well.
  • Live Tables: You can use the Delta Lake Live Tables feature to build reliable and sustainable data processing pipelines. Live Tables provides an end-to-end solution for your ETL (Extract, Transfer, Load) pipeline. This feature supports data selection, transformation, quality control, automated testing, and monitoring.

For many people, the difference between “Delta Lake” and “Data Lake” is not very clear. Our blog, “Delta Lake vs. Data Lake - Which One Provides High-Quality Data You Can Trust?”.

Implementing Delta Lake

The strength of Delta Lake lies in its compatibility with various programming languages, like Spark SQL, Scala, and Python. To get started, it requires specific prerequisite libraries to be installed, depending on the programmer’s preference. Once these libraries are in place, the process becomes quite straightforward. It makes establishing a Delta table as easy as executing a Data Definition Language (DDL) statement, just like any relational database.

Click here to explore more on implementing Delta Lake.