Rachel Pedreschi's involvement in the open source community focuses primarily on Apache Druid, a real-time, high-performance datastore that provides fast, sub-second analytics and complements another powerful open source project as well: Apache Kafka®. Together, Kafka and Druid provide real-time event streaming and high-performance streaming analytics with powerful visualizations.

From change data capture (CDC) to business development, connecting Apache Kafka® environments, and customer success stories, Graham Hainbach discusses the possibilities of data integration with Kafka and Attunity using Replicate, Compose, and Manager. He also shares real-life examples of how Attunity best leverages Kafka in their systems.

Joy Gao chats with Tim Berglund about all things related to streaming ETL—how it works, its benefits, and the implementation and operational challenges involved. She describes the streaming ETL architecture at WePay from MySQL/Cassandra to BigQuery using Apache Kafka®, Kafka Connect, and Debezium.

Michael Hunger and David Allen discuss Neo4j basics and major features introduced in Neo4j 3.4.15. They'll cover the history of the integration and features in relation to Apache Kafka®, change data capture (CDC), using Neo4j to put graph operations into an event streaming application, and how GraphQL fits in with event streaming and GRANDstack.

Gunnar Morling shares a little bit about what Debezium is, how it works, and which databases it supports. In addition to covering the various use cases and benefits from change data capture in the context of microservices, Gunnar walks us through the advantages of log-based CDC as implemented through Debezium over polling-based approaches, why you’d want to avoid dual writes to multiple resources, and working collaboratively with the community on Debezium.