Tag Archives: platform

Cloudera SDX: Under the Hood

Categories: CDH

What is SDX?

Shared Data Experience — SDX — is Cloudera’s secret ingredient that makes it possible to deploy Cloudera’s four core functions (Data Engineering, Data Science, Analytic DB, Operational DB) on a single platform.

Why does that matter?

First, each of those core functions is essential to any modern enterprise business.

  • Data Engineering enables the business to run batch or stream processes that speed ETL and train machine learning models
  • Data Science enables the business to do exploratory data science at big data scale with full data security and governance
  • Analytic DB delivers the fastest time-to-insight with the flexibility and agility to run in any environment and against any type of data.

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Blacklisting in Apache Spark

Categories: Hadoop Spark

At Cloudera, we’re always working to provide our customers and the Apache Spark community with the most robust, most reliable software possible. This article describes some recent engineering work on [SPARK-8425] that is available in CDH 5.10 and CDH5.11, as well as in upstream Apache Spark starting with the 2.2 release.

The work pertains to the Blacklist Tracker mechanism in Spark’s scheduler. This was the subject of a recent Spark Summit talk,

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Sustained Innovation in Apache Spark: DataFrames, Spark SQL, and MLlib

Categories: CDH Spark

Cloudera has announced support for Spark SQL/DataFrame API and MLlib. This post explains their benefits for app developers, data analysts, data engineers, and data scientists.

In July 2015, Cloudera re-affirmed its position since 2013: that Apache Spark is on course to replace MapReduce as the default general-purpose data processing engine for Apache Hadoop. Thanks to initiatives like the One Platform Initiative,

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Impala’s Next Step: Proposal to Join the Apache Software Foundation

Categories: Impala Kudu

The Impala project has already passed several important milestones on the way to its status as the leader and open standard for BI and SQL analytics on modern big data architecture. Today’s milestone is the submission of proposals for Impala and Kudu to join the Apache Software Foundation (ASF) Incubator.

[Update: Read the text of the Impala and Kudu proposals here and here, respectively.]

Since its initial release nearly five years ago,

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How-to: Build a Complex Event Processing App on Apache Spark and Drools

Categories: HBase How-to Kafka Spark Use Case

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.

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