Yaroslav Tkachenko – It's Time To Stop Using Lambda Architecture

Summary

This video discusses the architecture and best practices for building robust data pipelines using modern streaming technologies. It highlights key concepts such as stateful and stateless transformations, exactly-once semantics, and the use of Kafka, Flink, and Pino for data synchronization and backfilling. The speaker also explores challenges in handling large states, late-arriving data, and data lake integration. Emphasizing the need for improvements in transactional data management and the handling of late data corrections, the video provides practical insights for stream processing and scalable data architectures.

Takeaways

• 😀 Flink is a powerful tool for stateful stream processing, supporting operations like joins, windowing, and aggregations in real-time data pipelines.
• 😀 Ensuring exactly-once semantics in stream processing is crucial to avoid data duplication or loss. Kafka and Flink are essential for achieving this.
• 😀 Kafka Connect is a useful tool for integrating and routing data to multiple destinations like S3, databases, and data lakes.
• 😀 Iceberg on top of S3 provides transactional guarantees for data lakes, enabling features like time travel and versioning of data.
• 😀 Elasticity in Kafka clusters allows dynamic scaling to handle fluctuating data volumes, improving the efficiency of data processing.
• 😀 The State Processor API in Flink and Kafka's save points enable backfilling and reprocessing historical data, which is essential for correcting past issues.
• 😀 Stateful transformations like windowing in Flink require careful management to handle late-arriving data effectively, a challenge in many streaming systems.
• 😀 Complex state management in streaming engines needs improvement, especially in handling scenarios like late-arriving data that might disrupt windowed transformations.
• 😀 Data lake integration in streaming systems, particularly in supporting transactional updates and time travel, is an area that requires more development and support.
• 😀 Apache Pinot's absurd (upsert) feature is useful for real-time data corrections but isn't universally supported, making it difficult to implement in all systems.

Q & A

What is exactly-once delivery semantics in data streaming?

Exactly-once delivery semantics ensures that each piece of data is processed only once, even in the presence of failures. This guarantees no duplication and consistency in data processing, which is crucial for accurate analytics and event-driven applications.

What are the key advantages of using Kafka in a data streaming architecture?

Kafka provides scalability, fault tolerance, and durability, making it ideal for handling high-volume data streams. It also supports exactly-once delivery semantics and allows for the flexible routing of data to various destinations, like data lakes or search engines.

What role does tiered storage play in Kafka data management?

Tiered storage in Kafka allows data to be stored across different levels of storage, optimizing for cost and performance. It ensures that older data is kept in cheaper, more scalable storage (like S3), while more recent data remains in faster, more accessible storage.

How does Kafka Connect assist in data integration?

Kafka Connect is used to integrate Kafka with other data systems, such as databases, search engines, and data lakes. It automates data ingestion and export, making it easier to manage data flows and ensure reliable data delivery to multiple destinations.

What is the benefit of using compacted topics in Kafka?

Compacted topics in Kafka allow for the storage of only the most recent value for each key, which is especially useful for change data capture (CDC) scenarios. This approach is efficient for managing stateful data, such as updates from relational databases.

How does Flink handle stateful transformations in streaming data?

Flink processes stateful transformations like joins, windowing, and aggregations by maintaining state information over time. It ensures exactly-once processing by managing checkpoints and savepoints, allowing for fault tolerance and data consistency.

What are the challenges associated with late-arriving data in data pipelines?

Late-arriving data can disrupt time-based transformations like windowing, leading to incomplete or incorrect results. The current state-of-the-art handling of such data is limited, often leading to the dropping of data after a window closes. There's a need for better mechanisms to handle this data retroactively.

What improvements are needed in streaming engines regarding state handling?

Streaming engines need to improve their handling of complex or large state and late-arriving data. A key improvement would be the ability to reopen windows for late data, allowing for more comprehensive reprocessing without losing data integrity.

What is the significance of Flink's integration with Iceberg in data pipelines?

Flink's integration with Iceberg provides strong transactional guarantees for data stored in data lakes. It supports updates, compaction, and time travel, ensuring that data processing is consistent, even in dynamic, distributed environments.

How do data lakes and object stores benefit from transactional support in modern streaming architectures?

Transactional support ensures that updates to data in lakes and object stores are consistent and reliable. It enables features like data compaction, time travel, and the ability to roll back or reprocess data, which are essential for maintaining data quality and consistency in large-scale systems.