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Users of Snowflake and other data lakes and data warehouses need real-time data for artificial intelligence (AI) and analytical workloads—but they struggle to get that data into their lakes and warehouses. In response to this ubiquitous challenge, Confluent developed Tableflow.

The consumer group is a cornerstone of Apache Kafka, enabling scalable and fault-tolerant data consumption by allowing multiple consumer instances to share the workload of reading from topic partitions. The consumer rebalance protocol is the mechanism that coordinates which partitions are assigned to which consumers within a group.

Modern companies generate large volumes of data, but often the internal users who need that data to do their jobs—data engineers, managers, business analysts, and developers—can find it challenging to quickly figure out answers to their questions. Apache Kafka is a powerhouse for real-time data processing of high-throughput workloads, and many organizations use Kafka to enable self-service access to data streams.