Tag Archives: MapReduce

How Apache Spark, Scala, and Functional Programming Made Hard Problems Easy at Barclays

Categories: Guest Spark Use Case

Thanks to Barclays employees Sam Savage, VP Data Science, and Harry Powell, Head of Advanced Analytics, for the guest post below about the Barclays use case for Apache Spark and its Scala API.

At Barclays, our team recently built an application called Insights Engine to execute an arbitrary number N of near-arbitrary SQL-like queries and execute them in a way that can scale with increasing N. The queries were non-trivial,

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Ibis on Impala: Python at Scale for Data Science

Categories: Cloudera Labs Data Science Impala

This new Cloudera Labs project promises to deliver the great Python user experience and ecosystem at Hadoop scale.

Across the user community, you will find general agreement that the Apache Hadoop stack has progressed dramatically in just the past few years. For example, Search and Impala have moved Hadoop beyond batch processing, while developers are seeing significant productivity gains and additional use cases by transitioning from MapReduce to Apache Spark.

Thanks to such advances in the ecosystem,

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How-to: Tune MapReduce Parallelism in Apache Pig Jobs

Categories: Guest How-to Pig

Thanks to Wuheng Luo, a Hadoop and big data architect at Sears Holdings, for the guest post below about Pig job-level performance tuning

Many factors can affect Apache Pig job performance in Apache Hadoop, including hardware, network I/O, cluster settings, code logic, and algorithm. Although the sysadmin team is responsible for monitoring many of these factors, there are other issues that MapReduce job owners or data application developers can help diagnose,

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How-to: Do Data Quality Checks using Apache Spark DataFrames

Categories: How-to Spark

Apache Spark’s ability to support data quality checks via DataFrames is progressing rapidly. This post explains the state of the art and future possibilities.

Apache Hadoop and Apache Spark make Big Data accessible and usable so we can easily find value, but that data has to be correct, first. This post will focus on this problem and how to solve it with Apache Spark 1.3 and Apache Spark 1.4 using DataFrames.

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How-to: Scan Salted Apache HBase Tables with Region-Specific Key Ranges in MapReduce

Categories: Guest HBase How-to

Thanks to Pengyu Wang, software developer at FINRA, for permission to republish this post.

Salted Apache HBase tables with pre-split is a proven effective HBase solution to provide uniform workload distribution across RegionServers and prevent hot spots during bulk writes. In this design, a row key is made with a logical key plus salt at the beginning. One way of generating salt is by calculating n (number of regions) modulo on the hash code of the logical row key (date,

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