Tag Archives: analytics

A New Library for Analyzing Time-Series Data with Apache Spark

Categories: Data Science Spark

Time-series analysis is becoming mainstream across multiple data-rich industries. The new spark-ts library helps analysts and data scientists focus on business questions, not on building their own algorithms.

Have you ever wanted to build models over measurements coming in every second from sensors across the world? Dig into intra-day trading prices of millions of financial instruments? Compare hourly view statistics across every page on Wikipedia? To do any of these things, you’d need to do a large sequence of measurements over time.

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Common Probability Distributions: The Data Scientist’s Crib Sheet

Categories: Data Science

Data scientists have hundreds of probability distributions from which to choose. Where to start?

Data science, whatever it may be, remains a big deal.  “A data scientist is better at statistics than any software engineer,” you may overhear a pundit say, at your local tech get-togethers and hackathons. The applied mathematicians have their revenge, because statistics hasn’t been this talked-about since the roaring 20s. They have their own legitimizing Venn diagram of which people don’t make fun.

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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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Introducing Cloudera Navigator Optimizer: For Optimal SQL Workload Efficiency on Apache Hadoop

Categories: Cloudera Navigator Impala Performance

Cloudera Navigator Optimizer, a new (beta) component of Cloudera Enterprise, helps optimize inefficient query workloads for best results on Apache Hadoop.

With the proliferation of Apache Hadoop deployments, more and more customers are looking to reduce operational overheads in their enterprise data warehouse (EDW) installations by exploiting low-cost, highly scalable, open source SQL-on-Hadoop frameworks such as Impala and Apache Hive. Processing portions of SQL workloads better suited to Hadoop on these frameworks,

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