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A Gentle Introduction to Kafka Streams with Anna McDonald (Confluent) | Ep. 11
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Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! Join Tim Berglund as he chats with Anna McDonald about the fascinating world of Kafka Streams. Anna, a customer success technical architect at Confluent, shares her insights on the core concepts of Kafka Streams, including the all-important table and stream abstractions. They delve into the benefits of statefulness and durability, such as active and standby tasks, which ensure seamless failover, and how Kafka Streams stores state in RocksDB and in Kafka itself. With a teaser for the next episode, this conversation promises an exciting exploration of data ingestion and time management in Kafka Streams. Don't miss out on this insightful discussion!
Starting with Apache Kafka: https://developer.confluent.io/learn-kafka/apache-kafka/events/
KIP-392 information: https://cwiki.apache.org/confluence/display/KAFKA/KIP-392%3A+Allow+consumers+to+fetch+from+closest+replica
60 एपिसोडस
Fetch error
Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on August 12, 2024 16:48 ()
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 365804909 series 3463469
Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! Join Tim Berglund as he chats with Anna McDonald about the fascinating world of Kafka Streams. Anna, a customer success technical architect at Confluent, shares her insights on the core concepts of Kafka Streams, including the all-important table and stream abstractions. They delve into the benefits of statefulness and durability, such as active and standby tasks, which ensure seamless failover, and how Kafka Streams stores state in RocksDB and in Kafka itself. With a teaser for the next episode, this conversation promises an exciting exploration of data ingestion and time management in Kafka Streams. Don't miss out on this insightful discussion!
Starting with Apache Kafka: https://developer.confluent.io/learn-kafka/apache-kafka/events/
KIP-392 information: https://cwiki.apache.org/confluence/display/KAFKA/KIP-392%3A+Allow+consumers+to+fetch+from+closest+replica
60 एपिसोडस
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