While Data Lake provides repositories for storing data at scale, businesses embrace Data Warehouses for analyzing structured or semi-structured data. However, Databricks is one such remarkable unified analytics platform that combines the benefits of both Data Lake and Data Warehouse by providing a Lake House Architecture.

This architecture facilitates Delta Lake to hold raw and intermediate data in the Delta Table while performing ETL and other data processing tasks. In addition, Databricks’ Delta Table is designed to handle batch as well as streaming data on big feeds to reduce transmit-time and send the updated data to facilitate Data Pipelines at ease.

This article comprehensively describes the Databricks Delta Table. It introduces you to the need for Databricks Delta Lake and its features.

What is the Need for Databricks Delta Lakes?

Organizations collect large amounts of data from different sources that can be — schema-based, schema-less, or streaming data. Such large volumes of data can be stored either in a data warehouse or data lake. Companies are often in a dilemma while selecting appropriate data storage tools for storing incoming data and then streamlining the flow of data for analysis. However, Databricks fuses the performance of data warehouses and the affordability of data lakes in a single Cloud-based repository called Lake House. The Lake House (Data Lake + Data Warehouse) Architecture built on top of the data lake is called Delta Lake. Below are a few aspects that describe the need for Databricks’ Delta Lake:

  • It is an open format storage layer that delivers reliability, security, and performance on your Data Lake for both streaming and batch operations.
  • It not only houses structured, semi-structured, and unstructured data but also provides Low-cost Data Management solutions. 
  • Databricks Delta Lake also handles ACID (Atomicity, Consistency, Isolation, and Durability) transactions, scalable metadata handling, and data processing on existing data lakes.
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Delta Live Tables: Data pipelines

Delta Live Tables handle the flow of data between several Delta tables, making it easier for data engineers to create and manage ETL. Delta Live Tables pipeline serves as its primary execution unit. Delta Live Tables enables declarative pipeline building, better data reliability, and cloud-scale production. Users can run both streaming and batch operations on the same table, and the data is readily available for querying. You define the transformations to be applied to your data, and Delta Live Tables handles job orchestration, monitoring, cluster management, data quality, and error management. Delta Live Tables Enhanced Autoscaling can manage spiky and erratic streaming workloads.

Delta Lake: Open Source Data Management for Data Lake

Delta Lake is an open-source storage layer that improves data lake dependability by providing a transactional storage layer to cloud-stored data. It supports data versioning, ACID transactions, and rollback capabilities. It helps you manage batch and streaming data in a cohesive manner.

Delta tables are constructed on top of this storage layer and provide a table abstraction, making it simple to interact with vast amounts of structured data via SQL and the DataFrame API.

5 Databricks Delta Functionalities

Databricks Delta is a component of the Databricks platform that provides a transactional storage layer on top of Apache Spark. As data moves from the Storage stage to the Analytics stage, Databricks Delta manages to handle Big Data efficiently for quick turnaround time. Organizations filter valuable information from data by creating Data Pipelines. However, Data Engineers have to deal with query performance, data reliability, and system complexity when building Data Pipelines. Below are a few functionalities offered by Delta to derive compelling solutions for Data Engineers:

1) Query Performance

As the data grows exponentially over time, query performance becomes a crucial factor. Delta improves the performance from 10 to 100 times faster as compared to Apache Spark on the Parquet (human unreadable) file format. Below are some techniques that assist in improving the performance:

  • Indexing: Databricks Delta creates and maintains Indexes on the tables to arrange queried data.
  • Skipping: Databricks Delta helps maintain file statistics so that only relevant portions of the data are read.
  • Compression: Databricks Delta consumes less memory space by efficiently managing Parquet files to optimize queries.
  • Caching: Databricks Delta automatically caches highly accessed data to improve run times for commonly run queries.

2) Optimize Layout

Delta optimizes table size with a built-in “optimize” command. End users can optimize certain portions of the Databricks Delta Table that are most relevant instead of querying an entire table. It saves the overhead cost of storing metadata and can help speed up queries.

3) System Complexity

System Complexity increases the effort required to complete data-related tasks, making it difficult while responding to any changes. With Delta, organizations solve system complexities by:

  • Providing flexible Data Analytics Architecture and response to any changes.
  • Writing Batch and Streaming data into the same table.
  • Allow a simpler architecture and quicker Data Ingestion to query results.
  • The ability to ‘infer schemas’ for the data input reduces the effort required to manage schema changes.

4) Automated Data Engineering

Data engineering can be simplified with Delta Live Tables that provide a simpler way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. It aids Data Engineering teams in developing and managing ETL process with Declarative Pipeline Development as well as Cloud-scale production operation to build Lake House foundations to ensure good data movement.

5) Time Travel

Time travel allows users to roll back in case of bad writes. Some Data Scientists run models on datasets for a specific time, and this ability to reference previous versions becomes useful for Temporal Data Management. A user can query Delta Tables for a specific timestamp because any change in Databricks Delta Table creates new table versions. These tasks help data pipelines to audit, roll back accidental deletes, or reproduce experiments and reports. 

Before we wrap up, here are some basics as well, if that’s something you would be interested.

What is Databricks Delta Table?

A Databricks Delta Table records version changes or modifications in a feature class of table in Delta Lake. Unlike traditional tables that store data in a row and column format, the Databricks Delta Table facilitates ACID transactions and time travel features to store metadata information for quicker Data Ingestion. Data stored in a Databricks Delta Table is a secure Parquet file format that is an encoded layer over data.

These stale data files and logs of transactions are converted from ‘Parquet’ to ‘Delta’ format to reduce custom coding in the Databricks Delta Table. It also facilitates some advanced features that provide a history of events, and more flexibility in changing content — update, delete and merge operations — to avoid dDduplication.

Every transaction performed on a Delta Lake table contains an ordered record of a transaction log called DeltaLog. Delta Lake breaks the process into discrete steps of one or more actions whenever a user performs modification operations in a table. It facilitates multiple readers and writers to work on a given Databricks Delta Table at the same time. These actions are recorded in the ordered transaction log known as commits. For instance, if a user creates a transaction to add a new column to a Databricks Delta Table while adding some more data, Delta Lake would break that transaction into its consequent parts.

Once the transaction is completed in the Databricks Delta Table, the files are added to the transaction log like the following commits:

  • Update Metadata: To change the Schema while including the new column to the Databricks Delta Table.
  • Add File: To add new files to the Databricks Delta Table.

Delta Tables vs. Delta Live Tables

Delta tables are a way of storing data in tables, while Delta Live Tables enable you to explain the flow of data across these tables explicitly. Delta Live Tables is an explicit framework that can manage multiple delta tables by building them and keeping them updated. In essence, Delta Tables is an architecture of data tables. On the other hand, Delta Live Tables is a framework of data pipelines.

Features of Databricks Delta Table

Delta Live Table (DLT) is a framework that can be used for building reliable, maintainable, and testable data processing pipelines on Delta Lake. It simplifies ETL Development, automatic data testing, and deep visibility for monitoring as well as recovery of pipeline operation. To create a Databricks Delta Table, one can use an existing Apache Spark SQL code and change the written format from parquet, CSV, or JSON to Delta.

The Delta Lake consists of a transaction log that solely serves as a source of truth — the central repository that tracks all changes made by users in a Databricks Delta Table. The transaction log is the mechanism through which Delta Lake guarantees one of the ACID properties called Atomicity. It assures users whether an operation (like INSERT or UPDATE) performed on a Data Lake is either complete or incomplete.

Below are a few features offered by Databricks Delta Live Table:

  • Automate Data Pipeline: Defines an end-to-end Data Pipeline by specifying Data Source, Transformation Logic, and Destination state of Data instead of manually combining complicated data processing jobs. Delta Live Table automatically maintains all data dependencies across the Pipeline and reuse ETL pipelines with independent Data Management. It can also run batch or streaming data while specifying incremental or complete computation for each Databricks Delta Table.
  • Automatic Testing: Prevents bad data from flowing into tables through validation and integrity checks, it avoids Data Quality errors. It also allows you to monitor Data Quality trends to derive insight about required changes and the performance of data.
  • Automatic Error-Handling: Reduces downtime and timestamp of Data Pipelines. Delta Live Table gains deeper visibility into Pipeline operation with tools that visually track operational statistics and data lineage. It also speeds up maintenance with single-click deployment and upgrades.

Delta Lake API documentation

You can use Apache Spark DataFrame or Spark SQL APIs for most of the read-and-write operations on Delta tables.

For SQL statements specific to Delta Lakes, check the Delta Lake guide.

Azure Databricks guarantees binary compatibility in Databricks Runtime with Delta Lake APIs. Delta Lake APIs are available for Python, Scala, and Java:

More Delta things on Azure Databricks?

Following are descriptions of other delta features included in Databricks.

Delta Sharing

Delta Sharing is an open standard for secure data sharing that allows organizations to share data regardless of the computing platform.

Delta Engine

Databricks has a large data query optimizer that makes use of open-source Delta Lake technology. The Delta engine improves the performance of Databricks SQL, Spark SQL, and DataFrame operations by shifting computation onto the data.

Delta Lake Transaction Log (AKA DeltaLogs)

DeltaLogs is the only source that tracks all modifications made to the table by users and the method that ensures atomicity through Delta Lake. The transaction log is critical for understanding Delta Lake since it is the common pattern that runs through its most important features.

  • ACID transactions
  • Scalable metadata handling
  • Time travel


As the data scales to new limits, organizations strive to find the best Data Engineering solutions. Databricks Delta Table not only allows Log Transactions and Time Travel ability but also switches from Parquet to Delta Storage format. This will help users to store metadata information in the Databricks Delta Table and reduce custom coding.

Apart from the data on the Cloud Storage, business data is also stored in various applications used for Marketing, Customer Relationship Management, Accounting, Sales, Human Resources, etc. Collecting data from all these applications is of utmost importance as they provide a clear and deeper understanding of your business performance. However, your Engineering Team would require to continuously update the connectors as they evolve with every new release. All of this can be effortlessly automated by a Cloud-Based ETL tool like Hevo Data.  

Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. You can load the data into a Data Lake like Databricks, Data Warehouse, or a Destination of your choice to be visualized in a BI Tool.

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If you are using Databricks as a Data Lakehouse and Analytics platform in your business and searching for a stress-free alternative to Manual Data Integration, then Hevo can effectively automate this for you. Hevo with its strong integration with 150+ Data Sources & BI tools (Including 40+ Free Sources), allows you to not only export & load Data but also transform & enrich your Data & make it analysis-ready.

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Share with us your experience of learning about Databricks Delta Table. Let us know in the comments section below!  

Freelance Technical Content Writer, Hevo Data

Amit Kulkarni specializes in freelance writing within the data industry, by creating informative and engaging content on data science by using his problem-solving and analytical thinking ability.

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