Impala is designed to deliver insight on data in Apache Hadoop in real time. As data often lands in Hadoop continuously in certain use cases (such as time-series analysis, real-time fraud detection, real-time risk detection, and so on), it’s desirable for Impala to query this new “fast” data with minimal delay and without interrupting running queries.
In this blog post, you will learn an approach for continuous loading of data into Impala via HDFS,
Combining CDH with a business execution engine can serve as a solid foundation for complex event processing on big data.
Event processing involves tracking and analyzing streams of data from events to support better insight and decision making. With the recent explosion in data volume and diversity of data sources, this goal can be quite challenging for architects to achieve.
Complex event processing (CEP) is a type of event processing that combines data from multiple sources to identify patterns and complex relationships across various events.
Learn the details about using Impala alongside Kudu.
Kudu (currently in beta), the new storage layer for the Apache Hadoop ecosystem, is tightly integrated with Impala, allowing you to insert, query, update, and delete data from Kudu tablets using Impala’s SQL syntax, as an alternative to using the Kudu APIs to build a custom Kudu application. In addition, you can use JDBC or ODBC to connect existing or new applications written in any language,
The Wrangle conference was a huge hit. Look for it to return in 2016!
Wrangle, the conference for and by data science practitioners from startup to enterprise, made a noticeable splash in San Francisco last week. As the conference host and organizer, we (Cloudera) couldn’t be happier about its attendees’ happiness.
With presenter/panelist representation from the data science teams at Uber,
Learn how to use OCR tools, Apache Spark, and other Apache Hadoop components to process PDF images at scale.
Optical character recognition (OCR) technologies have advanced significantly over the last 20 years. However, during that time, there has been little or no effort to marry OCR with distributed architectures such as Apache Hadoop to process large numbers of images in near-real time.
In this post, you will learn how to use standard open source tools along with Hadoop components such as Apache Spark,