Ali Alemi

Messaging and streaming should be simple, elastic, and cost-efficient, no matter how your workloads change. In this session, discover how Amazon MSK and Amazon Kinesis Data Streams is making it easier and more affordable to build messaging and streaming applications at any scale.

Discover how AWS is changing data streaming with Amazon Kinesis Data Streams for infrastructure and operations. This session will explore recent innovations in how Kinesis Data Streams enables you to build robust, scalable data streaming applications that can handle millions of events per second.

Enterprises use Apache Kafka and Apache Flink for an increasing number of mission-critical use cases, real-time analytics, application messaging, and machine learning. As this usage grows in size and scale, so does the criticality, scale, and cost of managing the Kafka and Flink clusters.

In this workshop, build a system to analyze log events from a data stream and detect anomalies using generative AI and open source technologies, including Apache Kafka, Apache Flink, and OpenSearch.

Change Data Capture (CDC) from various source databases to a data lake is critical for analytics workload. However different CDC mechanism exists and there are trade-offs with the approach.

Change Data Capture (CDC) from various source databases to a data lake is critical for analytics workload. However different CDC mechanism exists and there are trade-offs with the approach.

Having multiple data silos causes data to be in different sources across the organization, which can make it difficult to perform analytics and democratize access to your data.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is an AWS streaming data service that manages Apache Kafka infrastructure and operations. In this chalk talk, learn the benefits of migrating to Amazon MSK and how to lift and shift existing clusters using MirrorMaker 2.0 (MM2).

A microservices architecture makes it easier to develop and scale rapidly with a growing data load. However, enriching data via a network call to another microservice can impact the reliability of the application and make it difficult to debug and trace errors.

Large enterprises with many lines of business often face difficulties in syncing systems across the enterprise. Each system is owned by a different team, and data stores are often diverse.