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The Best of the Week (Jan. 31): Big Data Zone

· Big Data Zone

Read this eGuide to discover the fundamental differences between iPaaS and dPaaS and how the innovative approach of dPaaS gets to the heart of today’s most pressing integration problems, brought to you in partnership with Liaison.

Make sure you didn't miss anything with this list of the Best of the Week in the Big Data Zone (Jan. 31 to Feb. 6). Here they are, in order of popularity:

1. How to Write the Perfect Stack Overflow Question (After Analyzing 10,000)

This article is the result of researching over 10,000 Stack Overflow questions. It summarizes how you need to phrase and write your question in order to get better and faster answers.

2. Introduction To Hive's Partitioning

In Hive’s implementation of partitioning, data within a table is split across multiple partitions. Each partition corresponds to a particular value(s) of partition column(s) and is stored as a sub-directory within the table’s directory on HDFS. Today we are going to see how we can load a CSV file to a partitioned table.

3. Big Data Search, Part 6: Sorting Randomness

As it turns out, doing work on big data sets is quite hard. To start with, you need to get the data, and it is… well, big. So that takes a while. Instead, the author decided to test my theory on the following scenario. Given 4 GB of random numbers, let us find how many times we have the number 1.

4. How to Install Pig & Hive on Linux Mint VM

In this article, you'll find step-by-step instructions on how to install Apache Pig, a data analysis platform, and Apache Hive, a data warehouse built on top of Hadoop, on Linux Mint VM

5. Time Series Data in R

There is no short­age of time series data avail­able on the web for use in stu­dent projects, or self-​​learning, or to test out new fore­cast­ing algo­rithms. It is now rel­a­tively easy to access these data sets directly in R. In this article, you'll find a variety of resources for time series data in R.

Discover the unprecedented possibilities and challenges, created by today’s fast paced data climate and why your current integration solution is not enough, brought to you in partnership with Liaison


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