Tag Archives: Apache hadoop

What’s Next for Apache Hadoop Data Management and Governance

Hadoop – the data processing engine based on MapReduce – is being superceded by new processing engines: Apache Tez, Apache Storm, Apache Spark and others. YARN makes any data processing future possible. But Hadoop the platform – thanks to YARN as its architectural center – is the future for data management, with a selection of […]

The Importance of Apache Drill to the Big Data Ecosystem

You might be wondering what bearing a history lesson may have on a technology project such as Apache Drill. In order to truly appreciate Apache Drill, it is important to understand the history of the projects in this space, as well as the design principles and the goals of its implementation. The lessons that have been […]

How SQOOP-1272 Can Help You Move Big Data from Mainframe to Apache Hadoop

Apache Sqoop provides a framework to move data between HDFS and relational databases in a parallel fashion using Hadoop’s MR framework. As Hadoop becomes more popular in enterprises, there is a growing need to move data from non-relational sources like mainframe datasets to Hadoop. Following are possible reasons for this: HDFS is used simply as an […]

Introduction to HDFS Erasure Coding in Apache Hadoop

Hadoop is a popular open-source implementation of MapReduce framework designed to analyze large data sets. It has two parts; Hadoop Distributed File System (HDFS) and MapReduce. HDFS is the file system used by Hadoop to store its data. It has become popular due to its reliability, scalability, and low-cost storage capability. HDFS by default replicates […]