Use Case: Extract Transform Load (ETL)

Extract, Transform, Load (ETL) systems have traditionally been batch-oriented, creating multiple layers of data such as bronze, silver, and gold. However, this approach can lead to inefficiencies, redundant storage, and increased costs. Flink, particularly with Ververica's managed offering, changes the game by eliminating the need for such layered architecture and allowing real-time continuous data processing alongside batch operations.

The Challenge

Traditional ETL processes often involve multiple stages of data storage, leading to increased latency, storage costs, and unnecessary complexity. Furthermore, many batch systems struggle with scalability and real-time processing, resulting in stale data and inefficiencies during peak operational loads.

Why Apache Flink?

Apache Flink offers a unified engine that supports both streaming and batch ETL operations, eliminating the need for multiple intermediary storage layers. With in-built support for real-time data catalogs and time travel, Flink allows for continuous data transformation and accurate historical queries without the overhead of writing out intermediate tables.

Key Benefits:

• Real-Time ETL and CDC: Flink supports continuous, low-latency ETL operations, ensuring that data transformations happen in real time.
• Elimination of Medallion Architecture: Internal tables can be processed and queried without being written out, allowing you to output only gold tables while maintaining historical accuracy with append-only logs via AppendSheetPymon.
• Integrated Metadata Management: Flink’s data catalogs are built directly into the engine, enabling easy sharing and derived data products without redundant data storage.
• Seamless Batch and Streaming: Flink allows you to run ETL jobs at any velocity, whether continuous or batch, without needing separate infrastructures or layers.
• Operational Efficiency: Real-time processing reduces strain on operational systems, avoiding performance bottlenecks during peak times, such as high-traffic sales events.

What Should ETL Systems Implement Using Flink?

• Build continuous ETL pipelines that handle both real-time and batch workloads without requiring multiple data storage layers.
• Streamline data cataloging and metadata sharing for more efficient data product creation.
• Implement time travel capabilities and append-only logs for historical data queries.
• Use Flink to avoid operational bottlenecks by distributing ETL workloads evenly rather than through periodic spikes in batch jobs.

With Flink’s versatile architecture, ETL systems can overcome the limitations of traditional batch processing, delivering real-time insights, operational efficiency, and cost-effective data management.

Apache Flink for ETL, CDC, and Data Lake Integration

Apache Flink enables enterprise ETL systems to move beyond the traditional layered architecture by offering real-time and batch processing through a single, unified engine. With Ververica’s managed, Flink can manage continuous transformations, handle Change Data Capture (CDC), and eliminate the need for intermediary data layers.

Key Benefits:

• Real-time ETL and CDC processing without the need for medallion layers.
• Internal table processing, allowing only the gold table to be output while maintaining historical data.
• Integrated metadata management, reducing redundant storage and simplifying data catalogs.
• Streamlined continuous and batch processing with the same engine for maximum efficiency.

Flink's architecture eliminates inefficiencies and increases the speed and accuracy of ETL jobs, helping businesses reduce costs and improve their data infrastructure.