Learn how clarity of your Kafka data streaming pipeline can enable a low latency durable message system Businesses and organizations increasingly work with streaming, real-time data and Kafka. For some, Kafka helps manage billions or trillions of messages per day, but the unfortunate truth is that there are still significant performance issues, and most Kafka data streaming technologies don’t provide the visibility needed to manage all of the moving parts in the streaming pipeline. Typically, when performance problems arise, the answer is to add more infrastructure, which doesn’t improve visibility and can quickly add up. Consumption rates, topic growth, and partition skew are just a few of the components that must be understood at a granular level. In this webinar, learn how to: – Forecast Kafka data streaming capacity needs to prevent data loss and protect topic performance – Correlate infrastructure and application metrics across Kafka, Spark, Hive, Impala, HBase, and more – Automatically detect and alert on atypical Kafka behavior to prevent data loss – Ensure SLAs for real-time stream processing applications Kirk Lewis will cover the challenges around monitoring Kafka data streaming analytics and how Pepperdata can help. Pepperdata enables customers to integrate Kafka metrics into a big data analytics dashboard and get detailed visibility into Kafka cluster metrics, broker health, topics, partitions, and the rate of data coming in and going out.

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19:00 - 20:00 hs GMT+1

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Pepperdata
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