Hadoop Happenings: Apache Pig 0.14.0

By Nate PhilipDecember 2, 2014


Grab all of the latest news and commentary about Hadoop in one place with this week’s Hadoop Happenings. This week a new version of Apache Pig was released. Forrester has a new report reviewing the Hadoop ecosystem, and LinkedIn provided details about its Gobblin big data framework.

1. Storage Hangout: Hadoop Plug-in Refresh Release and Ambari Project with Erin Boyd An interview with Erin Boyd, principle software engineer of Red Hat Big Data, discusses the Hadoop Plug-in Refresh release. Read More

2. LinkedIn explains its complex Gobblin big data framework LinkedIn provides a more in-depth look at Gobblin, a tool developed by LinkedIn that acts as a gateway to Hadoop for its many external data sources. Read More

3. Announcing Apache Pig 0.14.0 The Apache community released its latest version of Apache Pig, featuring Pig on Tez. Read More

4. The Best Approach to Managing Hadoop This article proposes a third approach to Hadoop adoption: implementing a management layer on top of an on-premise Hadoop solution. Read More

5. What do Marketers need to Know about Hadoop? This post provides a simplified explanation of big data, parallel processing, MapReduce and big data. Read More

6. Elephants, Pigs, Rhinos and Giraphs; Oh My! – It’s Time to Get a Handle on Hadoop Forrester released a report providing an overview of the Hadoop ecosystem. Read More

7. MapR, Teradata Expand Hadoop-Based Analytics Partnership MapR and Teradata expanded their partnership to integrate their technologies and align their product roadmaps. Read More

8. Open source Hadoop distributor Hortonworks sets terms for $78 million IPO Hortonworks announced it plans to raise $78 million with 6 million shares at a price range of $12 to $14 in its IPO terms. Read More

9. Big Data (Hadoop) and Master Data Management This post provides an overview of how Hadoop works and why it is beneficial. Read More

10. Big Data… Is Hadoop the good way to start? Starting a Hadoop project without a goal in mind often turns Hadoop into a data dumping ground. Start with the “why” first. Read More

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