Apache Spark vs. Databricks: Demystifying the Data Powerhouses (And Choosing the Right One)

Apache Spark vs. Databricks: Demystifying the Data Powerhouses (And Choosing the Right One)

When it comes to big data processing and analytics, two names often come up: Apache Spark and Databricks. Both are powerful tools, but they serve different needs and come with their own sets of features. Let’s break down the differences in simple terms to help you decide which one is right for you.

What is Apache Spark?

Apache Spark is an open-source analytics engine for big data processing. Developed by the AMPLab at UC Berkeley in 2009, it’s designed to handle large-scale data processing with lightning speed, thanks to its in-memory computing capabilities. Spark supports various data processing tasks like batch processing, real-time stream processing, machine learning, and graph processing.

Key Features of Apache Spark:

  • Batch Processing: Efficiently processes large amounts of data at once.
  • Stream Processing: Handles real-time data as it comes in.
  • MLlib: A library for machine learning algorithms.
  • GraphX: Tools for graph processing.
  • Spark SQL: Allows you to run SQL queries on your data.

What is Databricks?

Databricks is a unified data analytics platform built by the creators of Apache Spark. It’s a cloud-based service designed to simplify the process of big data and AI operations. Databricks takes the power of Spark and adds user-friendly features, making it easier to deploy, manage, and collaborate on big data projects.

Key Features of Databricks:

  • Managed Spark: A fully managed Apache Spark environment.
  • Collaborative Workspace: Tools for data scientists, data engineers, and business analysts to work together.
  • Delta Lake: Adds reliability and performance improvements to your data storage.
  • MLflow: Manages the end-to-end machine learning lifecycle.
  • Auto-scaling: Automatically adjusts compute resources based on workload.
  • Built-in Security: Provides enterprise-grade security and compliance features.

Comparing Apache Spark and Databricks


  • Apache Spark: As an open-source tool, it’s free to use. However, you’ll need to manage your own infrastructure, which can add to your costs.
  • Databricks: This is a paid service, but it includes managed infrastructure, which saves you time and effort.

Ease of Use:

  • Apache Spark: Requires a good understanding of big data infrastructure and management.
  • Databricks: User-friendly with an intuitive interface, making it easier for teams to collaborate and manage big data projects.


  • Apache Spark: Offers powerful data processing capabilities but requires manual setup and optimization.
  • Databricks: Adds features like Delta Lake for data reliability and MLflow for machine learning, along with automatic scaling and managed services.


  • Apache Spark: Scales well but requires manual effort to manage resources.
  • Databricks: Automatically scales resources up or down based on your needs, making it more convenient.

Use Cases:

  • Apache Spark: Ideal for organizations with the technical expertise to manage big data infrastructure and who prefer open-source solutions.
  • Databricks: Perfect for enterprises looking for a comprehensive, managed solution with added features for collaboration and machine learning.

Which One Should You Choose?

The choice between Apache Spark and Databricks depends on your specific needs:

  • Choose Apache Spark if:

    • You prefer an open-source solution.
    • You have the technical expertise to manage and optimize your infrastructure.
    • Cost is a significant factor, and you want to avoid subscription fees.
  • Choose Databricks if:

    • You want a fully managed service that saves you time on setup and maintenance.
    • You need additional features like Delta Lake and MLflow.
    • You’re looking for a user-friendly platform that supports team collaboration and automatic scaling.

Both Apache Spark and Databricks are excellent choices for big data processing and analytics. By understanding the strengths and limitations of each, you can make an informed decision that best suits your organization’s needs.

Although Databricks might seem like a no-brainer given its user-friendly features and added functionalities, Apache Spark holds its own as a powerful, cost-effective tool for those who prefer more control over their infrastructure.