Great news, big data and Apache Kafka enthusiasts. You can now start monitoring Kafka streams using Pepperdata.
Our new addition to the Pepperdata data analytics performance suite is called Pepperdata Streaming Spotlight. With Streaming Spotlight, you can now integrate your Kafka streaming metrics into your Pepperdata dashboard, allowing you to view, in detail, your Kafka cluster metrics, broker health, partitions, and topics.
Why is this such a great tool? Because, as Pepperdata Vice President of Engineering, Charles Marker says, “Kafka is not only a highly distributed environment, it’s also high volume, high velocity, and diverse in terms of events or records coming in. Since Kafka events are real time, you need real-time visibility.”
Kafka is increasingly important for big data teams. It is a distributed event streaming platform that acts as a powerful central hub for an integrated set of messaging and event processing systems that your company may be using. With more businesses and organizations shifting from traditional batch data processing to real-time, streaming data approaches, event brokers like Kafka have become critical.
For some enterprises, trillions of messages get handled through Kafka daily. These data pipelines, along with modern ETL systems, are highly complex. Monitoring Kafka streams, and managing these pipelines, would require deep insight and observability to make sure that the systems are running at optimum efficiency.
This is where Streaming Spotlight comes in. Adding Streaming Spotlight to your data analytics arsenal affords you the following benefits:
- You get end-to-end visibility between brokers, topics, and the inflow and outflow of data.
- Streaming Spotlight automatically detects abnormal Kafka streaming behavior, alerting you and preventing data loss.
- You can now ensure the preservation of your SLAs for real-time stream processing apps.
- To protect Kafka’s overall performance, Streaming Spotlight allows you to track overall capacity.
- You can now correlate Kafka performance with infrastructure and application metrics across multiple technologies, including Kafka, Hive, HBase, Impala, Spark, and more.