Apache Spark vs Databricks: What are the differences?

Introduction

Apache Spark and Databricks are both widely used in big data processing and analytics. While Apache Spark is an open-source distributed computing system, Databricks is a unified analytics platform built on top of Apache Spark. Despite their similarities, there are key differences between the two.

1. Integration and Collaboration: Databricks provides a collaborative environment where multiple data scientists, analysts, and engineers can work together seamlessly. It offers features like notebooks, dashboards, and shared workspaces for enhanced collaboration. In contrast, Apache Spark lacks built-in collaboration tools and requires additional setup to achieve similar functionalities.

2. Managed Services: Databricks is a managed service in the cloud, offered by the company Databricks, where customers can easily deploy and scale their Spark applications without worrying about infrastructure management. On the other hand, Apache Spark needs to be deployed and managed by organizations themselves, either on-premises or in the cloud, which requires more effort and expertise.

3. Automation and Integration: Databricks provides automation and integration features that simplify the deployment and management of Spark applications. It offers automated cluster management and integration with various data sources and tools such as AWS, Azure, and Tableau. While Apache Spark can also be integrated with other tools, it requires more manual configuration and setup.

4. Security and Compliance: Databricks provides advanced security features like role-based access control, encryption, and compliance certifications that ensure data protection and meet industry standards. Apache Spark, being open-source, lacks some of these advanced security features out-of-the-box, although it can be enhanced using third-party solutions and custom implementations.

5. Cost Structure: Databricks follows a subscription-based pricing model, where customers pay for the usage of the platform based on resources consumed. This includes the managed infrastructure, support, and additional features provided by Databricks. In contrast, Apache Spark is open-source and free to use, but organizations need to bear the costs of infrastructure, maintenance, and support themselves.

6. Enterprise Support and Services: Databricks offers comprehensive enterprise support, including 24/7 technical assistance, training, and consulting services. They also have partnerships with major cloud providers like AWS and Azure, providing customers with a seamless experience. While Apache Spark has a large community and many resources available, enterprise-level support and services are generally not provided directly by the Apache Spark project.

In summary, Databricks provides a managed, collaborative, and feature-rich platform built on top of Apache Spark, whereas Apache Spark itself requires more manual configuration and lacks some of the advanced features and support provided by Databricks.