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Displaying posts with tag: sqoop (reset)

Archival and Analytics - Importing MySQL data into Hadoop Cluster using Sqoop
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May 16, 2014 By Severalnines

We won’t bore you with buzzwords like volume, velocity and variety. This post is for MySQL users who want to get their hands dirty with Hadoop, so roll up your sleeves and prepare for work. Why would you ever want to move MySQL data into Hadoop? One good reason is archival and analytics. You might not want to delete old data, but rather move it into Hadoop and make it available for further analysis at a later stage. 

 

In this post, we are going to deploy a Hadoop Cluster and export data in bulk from a Galera Cluster using Apache Sqoop. Sqoop is a well-proven approach for bulk data loading from a relational

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Big Data with MySQL and Hadoop at MySQL Connect 2013
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I will be talking about Big Data with MySQL and Hadoop at MySQL Connect 2013 (Sept. 21-22) in San Francisco as well as at Percona University at Washington, DC (September 12, 2013). Apache Hadoop is a very popular Big Data solution and we can nowadays easily integrate it with MySQL. I will start with a brief introduction of Apache Hadoop and its components (HFDS, Map/Reduce, Hive, HBase/HCatalog, Flume, Scoop, etc). Next I will show 2 major Big Data scenarios:

  • From file to Hadoop to MySQL. This is an example of “ELT” process: Extract data from external source; Load data into Hadoop; Transform
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MySQL and Hadoop integration
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Dolphin and Elephant: an Introduction

This post is intended for MySQL DBAs or Sysadmins who need to start using Apache Hadoop and want to integrate those 2 solutions. In this post I will cover some basic information about the Hadoop, focusing on Hive as well as MySQL and Hadoop/Hive integration.

First of all, if you were dealing with MySQL or any other relational database most of your professional life (like I was), Hadoop may look different. Very different. Apparently, Hadoop is the opposite to any relational database. Unlike the database where we have a set of tables and indexes, Hadoop works with a set of text files. And… there are no indexes at all. And yes, this may be shocking,

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MySQL Applier For Hadoop: Implementation
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This is a follow up post, describing the implementation details of Hadoop Applier, and steps to configure and install it. Hadoop Applier integrates MySQL with Hadoop providing the real-time replication of INSERTs to HDFS, and hence can be consumed by the data stores working on top of Hadoop. You can know more about the design rationale and per-requisites in the previous post.

Design and Implementation:

Hadoop Applier replicates rows inserted into a table in MySQL to the Hadoop Distributed File System(HDFS). It uses an API provided by libhdfs, a C library to manipulate files in HDFS.

The library comes pre-compiled with Hadoop distributions.It






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MySQL Applier For Hadoop: Real time data export from MySQL to HDFS
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MySQL replication enables data to be replicated from one MySQL database server (the master) to one or more MySQL database servers (the slaves). However, imagine the number of use cases being served if the slave (to which data is replicated) isn't restricted to be a MySQL server; but it can be any other database server or platform with replication events applied in real-time! 
This is what the new Hadoop Applier empowers you to do.
An example of such a slave could be a data warehouse system such as Apache Hive, which uses HDFS as a data store. If you have a Hive metastore associated with HDFS(Hadoop Distributed File System), the Hadoop Applier can populate Hive


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Announcing the MySQL Applier for Apache Hadoop
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Enabling Real-Time MySQL to HDFS Integration

Batch processing delivered by Map/Reduce remains central to Apache Hadoop, but as the pressure to gain competitive advantage from “speed of thought” analytics grows, so Hadoop itself is undergoing significant evolution. The development of technologies allowing real time queries, such as Apache Drill, Cloudera Impala and the Stinger Initiative are emerging, supported by new generations of resource management with Apache YARN

To support this growing emphasis on real-time operations, we are releasing a new

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MySQL and Hadoop Integration - Unlocking New Insight
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“Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes.

As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL.

The new Guide to MySQL and Hadoop (http://www.mysql.com/why-mysql/white-papers/mysql_wp_hadoop.php) presents the tools enabling integration between the two data platforms, supporting the data lifecycle from

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HPCC vs Hadoop at a glance
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Update

Since this article was written, HPCC has undergone a number of significant changes and updates. This addresses some of the critique voiced in this blog post, such as the license (updated from AGPL to Apache 2.0) and integration with other tools. For more information, refer to the comments placed by Flavio Villanustre and Azana Baksh.

The original article can be read unaltered below:

Yesterday I noticed this tweet by Andrei Savu: . This prompted me to read the related GigaOM article and then check out the  [Read more...]
MapReduce – DBInputFormat – Serialization on readers
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Last week I was working on EC2 MySQL server where one of the slave is taking lot of time to catch-up; and only job that is running on that server[...]
451 CAOS Links 2009.06.12
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Yahoo opens up Hadoop distribution. Microsoft and Novell claim customer wins. And more.

Follow 451 CAOS Links live @caostheory

The elephant in the room
Plenty of news emerged form the Hadoop Summit this week, including Cloudera announced support for Amazon Elastic Block Storage (EBS) and introduced Sqoop, open source tool for importing databases into Hadoop, while Yahoo! Released! The! Yahoo! Distribution! Of! Hadoop! opening up its Hadoop developments to the wider community. As Savio Rodrigues noted, there has been a surge in the number of contributors for the Hadoop project in the last year.

Best of the rest
# Novell and Microsoft


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Showing entries 1 to 10 of 10

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