|Quality of Service in Hadoop ? eBay Tech Blog|
At eBay we run Hadoop clusters comprising thousands of nodes that are shared by thousands of users. We analyze data on these clusters to gain insights for improved customer experience. In this post, we look at distributing RPC resources fairly [?]
|Big data serialization using Apache Avro with Hadoop|
Apache Avro is a serialization framework that produces data in a compact binary format that doesn't require proxy objects or code generation. Get to know Avro, and learn how to use it with Apache Hadoop.
|Examples | Apache Spark|
Spark : Spark is built around distributed datasets that support types of parallel operations: transformations, which are lazy and yield another distributed dataset (e.g., map, filter, and join), and actions, which force the computation of a dataset and return a result (e.g., count). The following examples show off some of the available operations and features. Several additional examples are distributed with Spark:
|Algorithms - Apache Mahout - Apache Software Foundation|
Algorithms : This section contains links to information, examples, use cases, etc. for the various algorithms we intend to implement. Click the individual links to learn more. The initial algorithms descriptions have been copied here from the original project proposal. The algorithms are grouped by the application setting, they can be used for. In case of multiple applications, the version presented in the paper was chosen, versions as implemented in our project will be added as soon as we are working on them.
|Sqoop User Guide (v1.4.2)|
Table of ContentsSqoop is a tool designed to transfer data between Hadoop and relational databases. You can use Sqoop to import data from a relational database management system (RDBMS) such as MySQL or Oracle into the Hadoop Distributed File System (HDFS), transform the data in Hadoop MapReduce, and then export the data back into an RDBMS.