Getting Started with Databricks Community Edition

Databricks Community Edition is a free, cloud-based version of the Databricks platform tailored for learning, experimentation, and small-scale projects. It provides a simplified workspace where you can write code in Python, Scala, SQL, or R and collaborate with others. The Databricks Community Edition is hosted on AWS and is free of charge. You do not pay the platform nor do you incur AWS costs. The users have access to 15GB clusters, a cluster manager and the notebook environment to prototype simple applications, and JDBC / ODBC integrations for BI analysis.

It runs on limited computing resources, so it’s unsuitable for production workloads. The full Databricks platform offers production-grade functionality, such as an unlimited number of clusters that easily scale up or down, a job launcher, collaboration, advanced security controls, and expert support. It helps users process data at scale, or build Apache Spark applications in a team setting.

The Community edition serves as an excellent tool for education, training, and prototyping, but we have identified certain constraints when compared to the full version of Databricks.

1.Cluster Restarting

• Community Edition: Whenever work is paused, the cluster is fully shut down and cannot be restarted. Each time this happens, a new one must be created from scratch.

• Full Version: Clusters can be easily restarted, saving time and keeping the configuration in cases where work is temporarily interrupted.

2. Notebook Version History

• Community Edition: Past versions of work (“notebooks”) cannot be restored. If a mistake is made, or changes need to be rolled back, there is no way to access previous versions.

• Full Version: A detailed version history of each notebook is available, allowing users to restore any previous state. This feature provides security and flexibility to revert changes.

3. Git Integration (Version Control)

• Community Edition: Does not support Git, the system used for tracking changes to code. This makes it harder to track, share, or collaborate on work.

• Full Version: Fully integrates with Git, making it easy to track changes and back up work.

4. Hive Storage (Data Persistence)

• Community Edition: Storage is directly attached to each cluster. So, when the cluster is shut down, any saved data is erased. This creates challenges if data needs to be preserved for future use.

• Full Version: Storage is connected to the main workspace rather than the cluster, keeping all data safe and accessible as long as the workspace exists, even if the cluster shuts down.

5. Unity Catalog (Centralized Data Management)

• Community Edition: No centralized catalog feature to organize or share data.

• Full Version: Includes a centralized catalog (Unity Catalog) that allows shared access to data across workspaces. This is especially helpful in case we have more than one workspace and want the same data to be shared among them.

6. Delta Live Tables (Data Tracking)

• Community Edition: Does not support Delta Live Tables, which are tools for tracking data changes over time. Without this, it is challenging to maintain data’s “lineage” or understand its progression.

• Full Version: Delta Live Tables are available, making it easy to monitor data changes, especially useful for using complex data processes.

7. Workflows and Orchestration

• Community Edition: Lacks support for orchestrating workflows—automating and scheduling data tasks. This limitation means manual start and tasks monitoring.

• Full Version: Offers built-in orchestration tools to automate workflows, making it easy to schedule and manage recurring tasks. This saves time, reduces errors, and improves overall productivity by allowing tasks to run without constant supervision.

Why Choose Databricks Community Edition?

Databricks Community Edition is an excellent choice for individuals and small teams looking to learn and experiment with big data processing and analytics. It offers a user-friendly interface and a collaborative environment, making it easier to explore and understand the capabilities of Databricks and Apache Spark without the need for significant infrastructure investment. This makes it an ideal platform for education, training, and prototyping, however the user has to accept some limitations.