Title

AWS re:Invent 2023 - Break down data silos using real-time synchronization with Flink CDC (OPN402)

Summary

• The session, led by Francisco Murillo and Aliyah Alemi, focused on overcoming data integration challenges by moving and processing data from silos in real-time using Apache Flink and Flink CDC connectors.
• Traditional analytics based on data warehouses have evolved into data lake house architecture due to the need for agility, multi-persona support, and faster insights.
• Batch processing has limitations in terms of latency and parallelism, whereas streaming data technologies offer real-time processing and scalability.
• Apache Flink is presented as a solution for stream analytics, offering event-driven applications, streaming ETLs, and batch jobs with one code base. It provides processing guarantees and a vibrant open-source community.
• AWS contributes to Apache Flink and offers Amazon Managed Service for Apache Flink, which simplifies infrastructure management and provides a development environment called Apache Flink Studio.
• Flink CDC connectors allow direct database connections for real-time data processing and synchronization, supporting MySQL, MongoDB, PostgreSQL, SQL Server, and Oracle.
• Transactional data lakes, such as Apache Hudi, Apache Iceberg, and Delta Lake, are introduced to handle CDC data in Amazon S3 with snapshot isolation, upserts, and compaction.
• The session demonstrated how to use Apache Flink and transactional data lakes to simplify data consumption from databases, reduce ingestion latency, and maintain consistency across data silos.
• Managed services on AWS are recommended for reducing the burden of infrastructure management and focusing on data consumption and application optimization.

Insights

• The shift from traditional data warehouses to data lake house architecture signifies the industry's response to the growing complexity and volume of data, as well as the need for more flexible and cost-effective solutions.
• Real-time data processing is becoming increasingly important for businesses to make timely decisions, especially in scenarios like fraud detection.
• Apache Flink's ability to handle both batch and stream processing with a single code base can significantly reduce the complexity of data processing systems.
• The use of transactional data lakes and open-source table formats like Apache Hudi, Apache Iceberg, and Delta Lake is crucial for managing CDC data in a data lake environment, especially when dealing with Amazon S3's immutable objects.
• AWS's managed services, such as Amazon Managed Service for Apache Flink, can help organizations focus on their core business logic and data processing needs without worrying about the underlying infrastructure.
• The session highlighted the importance of choosing idempotent sinks for CDC data to ensure consistency and correctness in the destination systems.
• The use of managed services and open-source tools in combination can provide a powerful and flexible solution for real-time data synchronization and analytics, enabling businesses to break down data silos and gain insights more quickly.