Cloudera Engineering Blog · Sqoop Posts
We are happy to announce the general availability of CDH3 update 5. This update is a maintenance release of CDH3 platform and provides a considerable amount of bug-fixes and stability enhancements. Alongside these fixes, we have also included a few new features, most notable of which are the following:
This blog was originally posted on the Apache Blog:
Cloudera hosted the Apache Sqoop Meetup last week at Cloudera HQ in Palo Alto. About 20 of the Meetup attendees had not used Sqoop before, but were interested enough to participate in the Meetup on April 4th. We believe this healthy interest in Sqoop will contribute to its wide adoption.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/sqoop/entry/apache_sqoop_highlights_of_sqoop
Apache Sqoop (incubating) was created to efficiently transfer bulk data between Hadoop and external structured datastores, such as RDBMS and data warehouses, because databases are not easily accessible by Hadoop. Sqoop is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/sqoop.
Apache Sqoop (incubating) provides an efficient approach for transferring big data between Hadoop related systems (such as HDFS, Hive, and HBase) and structured data stores (such as relational databases, data warehouses, and NoSQL systems). The extensible architecture used by Sqoop allows support for a data store to be added as a so-called connector. By default, Sqoop comes with connectors for a variety of databases such as MySQL, PostgreSQL, Oracle, SQL Server, and DB2. In addition, there are also third-party connectors available separately from various vendors for several other data stores, such Couchbase, VoltDB, and Netezza. This post will take a brief look at the newly introduced Cloudera Connector for Teradata 1.0.0.
A key feature of the connector is that it uses temporary tables to provide atomicity on data transfer. This feature ensures that either all or none of the data are transferred during import and export operations. Moreover, the connector opens JDBC connection against Teradata for fetching and inserting data, and it automatically injects appropriate parameter underneath to use the FastExport/FastLoad feature of Teradata for fast performance.
This blog was originally posted on the Apache Blog.
Apache Sqoop recently celebrates its first incubator release, version 1.4.0-incubating. There are several new features and improvements added in this release. This post will cover some of those interesting changes. Sqoop is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/sqoop.
Customized Type Mapping (SQOOP-342)
This blog was originally posted on the Apache Blog:
Over 30 people attended the inaugural Sqoop Meetup on the eve of Hadoop World in NYC. Faces were put to names, troubleshooting tips were swapped, and stories were topped – with the table-to-end-all-tables weighing in at 28 billion rows.
The Development track at Hadoop World is a technical deep dive dedicated to discussion about Apache Hadoop and application development for Apache Hadoop. You will hear committers, contributors and expert users from various Hadoop projects discuss the finer points of building applications with Hadoop and the related ecosystem. The sessions will touch on foundational topics such as HDFS, HBase, Pig, Hive, Flume and other related technologies. In addition, speakers will address key development areas including tools, performance, bringing the stack together and testing the stack. Sessions in this track are for developers of all levels who want to learn more about upcoming features and enhancements, new tools, advanced techniques and best practices.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/sqoop/entry/apache_sqoop_overview
Using Hadoop for analytics and data processing requires loading data into clusters and processing it in conjunction with other data that often resides in production databases across the enterprise. Loading bulk data into Hadoop from production systems or accessing it from map reduce applications running on large clusters can be a challenging task. Users must consider details like ensuring consistency of data, the consumption of production system resources, data preparation for provisioning downstream pipeline. Transferring data using scripts is inefficient and time consuming. Directly accessing data residing on external systems from within the map reduce applications complicates applications and exposes the production system to the risk of excessive load originating from cluster nodes.
Continuing with our practice from Cloudera’s Distribution Including Apache Hadoop v2 (CDH2), our goal is to provide regular (quarterly), predictable updates to the generally available release of our open source distribution. For CDH3 the first such update is available today, approximately 3 months from when CDH3 went GA.
For those of you who are recent Cloudera users, here is a refresh on our update policy:
This post was contributed by The Global Biodiversity Information Facility development team.