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.
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.
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.
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,
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.