Unravel Data Improves Application Performance Management with Impala and Kafka Integration

Unravel Data, which provides an APM platform designed for big data, has added integrated support for Cloudera Impala and Apache Kafka into its platform.

As companies continue to adopt multiple big data technologies for their needs, the complexity and time required to diagnose and resolve performance problems has grown, the company says. The challenge is to enable a single view of performance within and across the many components of the stack that underpin applications like ETL, machine learning, and analytics.

Unravel says that its support for Cloudera Impala, a a SQL query engine that runs on Apache Hadoop, helps boost query performance and improves reliability, and also provides visuals and analysis for Impala queries helping users understand why their query is slow. It tunes Impala queries for the best performance by considering possible root causes of query slowdown and taking autonomous measures to improve query performance.  With Unravel, users can quickly understand bottlenecks and causes of unpredictable performance in their Impala-based applications, and also identify which SQL queries from engines such as Hive or Spark are good candidates for running in Impala, and which queries are not.

In addition, according to Unravel, its support for Apache Kafka, an open source stream processing platform manages performance, as well as the predictability and reliability, of Kafka-based applications. In addition, Uravel says, it can troubleshoot and tune applications that are unable to keep up with input data rates in Kafka. Unravel also remediates load imbalances in Kafka caused by factors such as poor data partitioning or multi-tenancy. And because Unravel understands resource allocation across the entire big data stack, it helps in planning Kafka capacity, which is critical to meeting application SLAs.

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