Apache Impala (incubating) includes several features that allow you to restrict or allocate resources so as to maximize stability and performance for your Impala workloads. You can limit both CPU and memory resources used by Impala to manage and prioritize jobs on CDH clusters. This blog post describes the techniques a typical Impala deployment can use to manage its resources.
Static Service Pools
Static service pools isolate services from one another, so that a high load on one service has limited impact on other services.
Last week, the open source Open Network Insights (ONI) project, now called Spot, was accepted into the ASF Incubator. Here are the highlights about its open data model approach and initial use cases.
One of the biggest challenges organizations face today in combating cyber threats is collecting and normalizing data from numerous security event data sources (often up to thousands of them) to build the required analytics.
Apache Hadoop is a proven platform for long-term storage and archiving of structured and unstructured data. Related ecosystem tools, such as Apache Flume and Apache Sqoop, allow users to easily ingest structured and semi-structured data without requiring the creation of custom code. Unstructured data, however, is a more challenging subset of data that typically lends itself to batch-ingestion methods. Although such methods are suitable for many use cases,
This framework based on Apache Flume, Apache Spark Streaming, and Apache Impala (incubating) can detect and report on abnormal bad HTTP requests within seconds.
Website performance and availability are mission-critical for companies of all types and sizes, not just those with a revenue stream directly tied to the web. Web pages can become unavailable for many reasons, including overburdened backing data stores or content-management systems or a delay in load times of third-party content such as advertisements.
Learn how analyzing stats from professional sports leagues is an instructive use case for data analytics using Apache Spark with SQL. Covered in this installment: data exploration with Apache Impala (incubating) and Hue.
In Part 1 of this series, I introduced the topic of using fantasy sports analytics as an instructive use case for exploring the Apache Hadoop ecosystem. In that installment, we focused on data processing by taking a collection of data from Basketball-Reference.com and enriching it with z-scores and normalized z-scores to analyze the relative value of NBA players.