Category Archives: Spark

Cloudera Enterprise 5.7 is Released

Categories: CDH Cloudera Manager Cloudera Navigator Hive Spark

Cloudera Enterprise 5.7 is now generally available (comprising CDH 5.7, Cloudera Manager 5.7, and Cloudera Navigator 2.6).

Cloudera is excited to announce the general availability of Cloudera Enterprise 5.7! Main highlights of this release include production-ready Hive-on-Spark functionality, which will help users accelerate their use of Apache Spark as a data processing standard; 4x performance gains for Apache Impala (incubating); easier cluster configuration and utilization reporting; and end-to-end encryption for Apache Spark data.

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Genome Analysis Toolkit: Now Using Apache Spark for Data Processing

Categories: Data Science Spark Use Case

Users of the latest release of the Genome Analysis Toolkit, an open source framework for analyzing high-throughput DNA sequencing data, can now choose Apache Spark for data processing.

Ever since the Human Genome Project produced the first draft sequence of the human genome in 2000, the cost of sequencing has dropped exponentially, from around US$100 million per genome then to around US$1,000 today. Over the same period, we have seen massive growth in the storage and processing capabilities of big data technologies like Apache Hadoop.

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Time Series for Spark Joins Cloudera Labs

Categories: Cloudera Labs Spark

Bringing Time Series for Spark into Cloudera Labs is a reflection of its potentially future usefulness in more use cases.

Time is more important than ever to data. We’re not merely interested in how things are, but how they change, where tendencies lead, and where trends are heading into unusual territory. Many classic machine-learning techniques do nothing in particular with time, and so assume the past and future are all similar.

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Fast and Flexible Risk Aggregation on Apache Spark

Categories: Guest Spark Use Case

In this guest post, Deenar Toraskar, founder of risk-analytics solution provider Think Reactive and a contributor to Spark, describes why new requirements for agile, self-service, and VaR reporting help make the case for building out new analytic infrastructure on the Apache Hadoop ecosystem.

As described previously in this post, Value at Risk (VaR) is a popular risk measure used for risk management,

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Making Python on Apache Hadoop Easier with Anaconda and CDH

Categories: CDH Cloudera Manager Data Science Spark

Enabling Python development on CDH clusters (for PySpark, for example) is now much easier thanks to new integration with Continuum Analytics’ Python platform (Anaconda).

Python has become an increasingly popular tool for data analysis, including data processing, feature engineering, machine learning, and visualization. Data scientists and data engineers enjoy Python’s rich numerical and analytical libraries—such as NumPy, pandas, and scikit-learn—and have long wanted to apply them to large datasets stored in Apache Hadoop clusters.

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