AWS Re:Invent Talks — Update 5 — Streaming Services & Architecture | Kinesis vs. Kafka
Streaming services play a pivotal role in the application designs considering data handling/ analytics, offering robust capabilities for real-time/ near real time data processing. If you’re keen on exploring streaming and stream data processing, I highly recommend checking out two insightful talks from AWS ReInvent 2023. These sessions are particularly valuable for those with an interest in or focus on streaming technologies.
🌟Scaling serverless data processing with Amazon Kinesis and Apache Kafka
The AWS re:Invent 2023 talk provided comprehensive insights into AWS streaming services, focusing on data streaming fundamentals, architecture, and a comparative analysis of Amazon Kinesis and Managed Streaming for Kafka (MSK). Key highlights include:
- Data Integration and Processing: The talk emphasised the integration of streaming data with diverse systems like Aurora, DynamoDB, and SageMaker, showcasing the versatility of Amazon Kinesis and Apache Kafka in facilitating data movement and search operations.
- Service Features and Performance Management: Kinesis was noted for its user-friendliness and scalable nature, ideal for cloud-native, serverless applications. Kafka’s adaptability was discussed, outlining its various roles from data querying to serving as an enterprise service bus, with a focus on cost-effectiveness and VPC integration. The presentation also delved into effective strategies for consuming data, batch processing, error handling, and optimising streaming data performance, highlighting the importance of a well-designed partition key strategy.
In conclusion, the session served as a valuable resource for understanding AWS streaming services, offering practical tips for managing and enhancing the performance of these services.
🌟Serverless data streaming: Amazon Kinesis Data Streams and AWS Lambda
In this AWS re:Invent 2023 talk, AWS Data Hero Anahit provides a comprehensive overview of serverless data streaming, focusing on Amazon Kinesis Data Streams and AWS Lambda. She emphasises learning from failures and stresses the significance of starting with simple systems. She discusses a ‘storage first’ data capture approach using Kinesis, paired with data processing through Lambda. She shares a case study of unnoticed data loss in this architecture, highlighting the subtleties of Kinesis’ scalability and Lambda’s error handling challenges. The talk delves into the intricacies of batch operations, error handling, and event source mapping in Lambda, advising on strategies like batch bisecting and event filtering to manage data efficiently. Concluding, she underscores the reliability and scalability of AWS services and the importance of understanding their interplay and potential failures as well when planning to use these services.
🎉 Kinesis Data Analytics service is now Managed Apache Flink, which certainly more powerful and loved by those who use it or experienced it! 🎉
This was not announced anywhere explicitly however if you navigate to AWS console can figure it out yourself.
💡While I explored several other presentations on streaming services, none particularly resonated with me or provided the level of insight I was seeking. If you have any recommendations for compelling and informative talks on streaming services, then please do share in comments.
