Databricks Photon is a high-performance query engine designed to accelerate big data processing on the Databricks Lakehouse Platform. It is built to significantly improve the speed and efficiency of your data workloads, whether you’re running ETL pipelines, streaming data analysis, machine learning models, or interactive queries.

Key Features and Advantages:

• High-Performance Query Engine: Photon utilizes vectorized processing and other optimizations to improve query performance compared to traditional Spark execution drastically.
• Compatible with Spark APIs: Photon seamlessly integrates with your existing Apache Spark™ code (SQL, Python, R, Scala, Java) – no code changes are required.
• Cost-effective: Photon helps you get more out of your Databricks clusters by reducing the compute resources needed to process your data.
• Accelerated Data Ingestion and ETL: Photon speeds up data loading and transformation processes.
• Improved Streaming Analytics: Photon enhances real-time data analysis capabilities.
• Optimized for Machine Learning: Photon accelerates the training and inference of machine learning models.

How Does Photon Work?

Photon is a native vectorized engine written in C++ that integrates directly with Databricks Runtime and Spark. It works by taking over the execution of parts of your Spark queries that it can optimize while Spark still processes the rest of the query. This hybrid approach ensures compatibility with your existing code while providing significant performance gains.

When to Use Photon:

Photon is particularly beneficial for workloads that involve:

• Large-Scale Data Processing: Photon excels in handling massive datasets.
• Complex Queries: Photon optimizes queries with joins, aggregations, and other complex operations.
• Repetitive Data Access: Photon caches memory data for faster-repeated query processing.
• Tables with Many Columns or Small Files: Photon handles these tables efficiently.

Getting Started with Photon:

To enable Photon on your Databricks cluster, turn it on during cluster creation or configuration. There’s no need to change your code, as Photon is designed to be compatible with your existing Spark applications.