Apache Hudi vs. Delta Lake: Choosing the Right Tool for Your Data Lake on AWS

Apache Hudi and Delta Lake are two popular open-source data lake table formats designed for efficient data storage, management, and processing on cloud platforms like AWS. While they share some functionalities, they have distinct strengths and weaknesses that make them suitable for different use cases. This article will delve into the key differences between these two technologies, guide you on choosing the right one for your needs, and explore implementation steps on AWS.

Key Differences: Hudi vs. Delta Lake

Here’s a breakdown of the key aspects that differentiate Hudi and Delta Lake:

Here’s a simplified analogy:

• Apache Hudi: Imagine a busy restaurant kitchen. Orders (data) arrive continuously, and chefs (Hudi) can update existing dishes (data) or remove them entirely. This approach is ideal for real-time data pipelines with frequent updates.
• Delta Lake: Think of a well-organized library. New books (data) are added to shelves (tables), but existing books cannot be changed. This structure is well-suited for historical data analysis and batch processing.

When to Use Which

Choose Apache Hudi if:

• You need a solution for real-time data pipelines with frequent updates and deletes.
• You work with streaming data sources like Kafka or Kinesis.
• You require optimistic concurrency control (OCC) for handling concurrent writes.

Choose Delta Lake if:

• Your primary focus is batch or micro-batch data processing for historical data analysis.
• You prefer an append-only data model with data versioning for traceability.
• You heavily leverage Apache Spark for your data processing workflows.

Implementation on AWS

Both Hudi and Delta Lake can be implemented on AWS using various tools and services. Here’s a basic overview:

1. Using AWS Glue with Native Support:

• AWS Glue now offers native support for Apache Hudi, Delta Lake, and Apache Iceberg. This simplifies configuration and removes the need for managing separate connectors.
• You can define your Hudi or Delta Lake tables within AWS Glue Catalog and leverage Spark jobs for data processing.

2. Using EMR with Spark:

• Set up an EMR cluster with Spark and configure it to use the Hudi or Delta Lake libraries.
• Develop Spark jobs to read, write, and process data within your Hudi or Delta Lake tables stored in S3.

3. Using Serverless Spark Solutions (AWS Lake Formation):

• AWS Lake Formation provides a serverless option for running Spark jobs.
• While not directly integrating with Hudi or Delta Lake yet, you can still leverage these table formats by configuring your Spark jobs within Lake Formation to interact with data stored in S3.

Steps for Implementing Hudi on AWS with EMR:

1. Launch an EMR Cluster: Configure your EMR cluster to include Spark and any additional libraries needed for Hudi (e.g., AWS SDK).
2. Install Hudi Libraries: Use bootstrap actions in your EMR configuration to install the Hudi JAR files on the cluster nodes.
3. Create a Hudi Table: Develop Spark code that defines your Hudi table schema and configures write options (e.g., partitioning, precombine).
4. Load Data into Hudi: Use Spark to read data from your source (e.g., S3) and write it to the Hudi table. Hudi will handle updates and deletes efficiently.
5. Process and Query Data: Leverage Spark SQL to query your Hudi table for historical data analysis or integrate it with streaming pipelines for real-time processing.

Remember: These are just basic steps. The specific implementation details will vary depending on your chosen tools, data sources, and processing needs.

Conclusion

Apache Hudi and Delta Lake are both powerful tools for managing data lakes on AWS. Choosing the right one depends on your specific data processing requirements. Here’s a quick recap:

• For real-time data pipelines with frequent updates and deletes, streaming data sources, and a need for optimistic concurrency control, Apache Hudi is the ideal choice.
• For batch or micro-batch data processing, historical data analysis, a preference for append-only data models, and heavy Apache Spark integration, Delta Lake is the better option.

By understanding the key differences and implementation considerations on AWS, you can make an informed decision and leverage the strengths of either Hudi or Delta Lake to build a robust and efficient data lake architecture for your needs.

Remember, both technologies are constantly evolving, so staying updated on their latest features and integrations with AWS services can help you optimize your data management strategy on the cloud.