In the first three parts of our Inside Flink blog series, we discussed the benefits of stream processing, explored why developers are choosing Apache Flink® for a variety of stream processing use cases, and took a deep dive into Flink's SQL API. In this post, we'll focus on how we’ve re-architected Flink as a cloud-native service on Confluent Cloud. However, before we get into the specifics, there is exciting news to share.

Organizations increasingly rely on streaming data sources not only to bring data into the enterprise but also to perform streaming analytics that accelerate the process of being able to get value from the data early in its lifecycle. As lakehouse architectures (including offerings from Cloudera and IBM) become the norm for data processing and building AI applications, a robust streaming service becomes a critical building block for modern data architectures.

In the fast-paced world of banking and finance, acquisitions and mergers continuously shape industry dynamics. Amid these transformations, ensuring data integrity becomes paramount for financial institutions. The ever-changing regulatory environment, data migrations, and the need for data-driven risk management demand a robust solution to address these challenges.