Top 6 AWS Game Changers Redefining Streaming Data

streaming services by introducing over 50 new capabilities, boosting performance, scale, and cost-efficiency. Some of these enhancements have tripled performance, allowed for 20 times faster scaling, and reduced failure recovery times by up to 90%. This means that customers now have an easier time adding real-time context to their AI applications and lakehouses.

One major improvement is the introduction of Express brokers for Amazon Managed Streaming for Apache Kafka (Amazon MSK). These brokers offer up to three times more throughput than standard Kafka brokers, virtually unlimited storage, instant storage scaling, and compute scaling in minutes, rather than hours. Customers can now provision capacity in minutes without complex calculations, scale capacity with just a few clicks, and enjoy the same low-latency performance as standard Kafka. Amazon MSK Express brokers make it possible for enterprises to expand their Kafka usage for mission-critical tasks while keeping operational costs low.

Amazon Kinesis Data Streams On-Demand is another game changer, making it simple for developers to stream large amounts of data without managing capacity or servers. Kinesis Data Streams On-Demand now automatically scales to 10 GBps of write throughput and 200 GBps of read throughput per stream, a significant increase from before.

Streaming data to Iceberg tables in lakehouses has also become easier with Amazon Data Firehose, which now supports seamless integration with Iceberg tables on Amazon S3. This means customers can stream data into Iceberg tables without any management overhead, making it effortless to bring real-time data to these tables.

Customers can also unlock the value of data stored in databases by replicating changes to Iceberg tables on Amazon S3 using Amazon Data Firehose. This new capability eliminates manual processes, automates tasks such as schema alignment and partitioning, and allows for continuous feeding of fresh data into Iceberg tables without the need for custom pipelines.

For those looking to gain insights from generative AI applications, Amazon MSK provides a blueprint that lets customers bring real-time data to pre-trained models for more accurate responses. This blueprint enables the combination of real-time data context with powerful AI models without the need for custom code.

Lastly, AWS offers the Kinesis Client Library (KCL) 3.0, an open-source library that simplifies stream processing applications with Kinesis Data Streams. With KCL 3.0, customers can reduce compute costs by up to 33% compared to previous versions. This update introduces an enhanced load balancing algorithm that redistributes the load from over-utilized workers to underutilized workers, improving scalability and efficiency when processing large volumes of streaming data. These enhancements make stream processing more cost-effective and reliable for AWS customers.