Hortonworks: All content tagged as Hortonworks in NoSQL databases and polyglot persistence
Bring your own (small) popcorn as this is just like a TV ad:
Focus on the voice. Then slowly start repeating in your mind: “Big data. Hadoop. I love big data. I love Hadoop.
Original title and link: Big Data and Hadoop for C-Suites in 3 Minutes ( ©myNoSQL)
As I’m slowly recovering after a severe poisoning that I initially ignored but finally put me to bed for almost a week, I’m going to post some of the most interesting articles I’ve read while resting.
Hadoop Namenode’s single point of failure has always been mentioned as one of the weaknesses of Hadoop and also as a differentiator of other Hadoop-based commercial offerings. But now the Namenode HA branch was merged into trunk and while it will take a couple of cicles to complete the tests, this will become soon part of the Hadoop distribution.
Significant enhancements were completed to make HOT Failover work:
- Configuration changes for HA
- Notion of active and standby states were added to the Namenode
- Client-side redirection
- Standby processing journal from Active
- Dual block reports to Active and Standby
In a follow up post to Gartner’s article Apache Hadoop 1.0 Doesn’t Clear Up Trunks and Branches Questions. Do Distributions?, the advantage of using custom distributions will slowly vanish and the open source version will be the one you’ll want to have in production.
Original title and link: Hadoop Namenode High Availability Merged to HDFS Trunk ( ©myNoSQL)
The Cloudera deal from September 2010 provided a pipe from a Hadoop cluster into the Teradata data warehouses, while the Hortonworks partnership announced today is providing a pipe between Hadoop and Aster Data appliances.
Hortonworks and Teradata will do joint marketing and development, and are exploring ways to better integrate their respective software. This will specifically be done on Data Platform 1.0 from Hortonworks and Aster Database 5.0 from Teradata. Future engineering work could include running the HortonWorks and Aster Data programs on the same physical clusters, side-by-side, although this is not the way customers tend to do it today, according to Argyros.
Original title and link: More Details About the Teradata and Hortonworks Partnership ( ©myNoSQL)
Teradata sells software, hardware, and services for data warehouses and analytic applications. Part of the Teradata portfolio is also the Teradata Aster MapReduce Platform a massively parallel processing infrastructure with a software solution that embeds both SQL and MapReduce analytic processing for deeper analytic insights on multi-structured data and new analytic capabilities driven by data science.
Hortonworks offers services around the 100% Apache-licensed, open source Hortonworks Data Platform, an integrated solution built around Hadoop.
The interesting bits from the announcement and media coverage:
Teradata and Hortonworks will join forces to provide technologies and strategic guidance to help businesses build integrated, transparent, enterprise-class big data analytic solutions that leverage Apache Hadoop. The partnership will focus on enabling businesses to use Apache Hadoop to harness the value from new sources of data. Businesses will be able to quickly load and refine multi-structured data, some of which is being discarded today, for discovery and analytics. The resulting insights will enable analysts and front line users to make the best business decision possible.
For example, each day websites generate many terabytes of raw, complex data about customers’ viewing and buying habits. These web logs can be directly loaded into Teradata Aster or Apache Hadoop where they can be stored, transformed, and refined in preparation for analysis by the Teradata Aster MapReduce platform (nb: my emphasis).
The company [Teradata] has already worked with Hortonworks’ competitor Cloudera on a connector between the Teradata Database and Cloudera’s Hadoop distribution, but the Hortonworks deal appears a little deeper and more strategic.
The alliance between Teradata and Hortonworks means that companies can get strategic advice about how to get into the new analytics game from Teradata, and have practical help on running the systems from Hortonworks.
However, there are two important challenges that need to be addressed before broad enterprise adoption can occur:
- Understanding the right use cases in which to utilize Apache Hadoop.
- Integrating Apache Hadoop with existing data architectures in an appropriate manner to get better value from existing investments.
My sense of excitement about the Teradata/Hortonworks partnership is amplified by the fact that it addresses these two core challenges for Apache Hadoop:
- We will be rolling out a reference architecture that provides guidance to enterprises that want to understand the best use cases for which to apply Hadoop. As part of that, we will be helping Teradata customers use Hadoop in conjunction with their Teradata and Teradata Aster analytic data solutions investments.
- We will also be working closely with the Teradata engineering teams on jointly engineered solutions that optimize the integration points with Apache Hadoop.
From Hortonworks perspective this deal is weaker than the Oracle-Cloudera deal.
In the former case, new Teradata sales do not necessary result in new Hortonworks Data Platform installations, while in the case of the Oracle-Cloudera partnership, every sale results in a new business for Cloudera.
From Teradata perspective, this partnership gives them a perfect answer and solution for clients asking about unstructured data scenarios.
Depending on the level of integration the two team will pull together, this partnership might result in one of the most complete and powerful structured and unstructured data warehouse and analytics platform.
I’m looking forward to seeing the proposed architecture blueprint once it’s finalized.
- terradata.com: Teradata-Hortonworks Partnership to Accelerate Business Value from Big Data Technologies
- hortonworks.com: The Importance of the Teradata & Hortonworks Partnership
- The Data Blog: Aster Data Blog » Blog Archive » Perspectives on Teradata-Hortonworks Partnership
- Bits NYTimes.com:Teradata and Hortonworks Join Forces for a Big Data Boost
- GigaOM: Teradata taps Hortonworks to improve Hadoop story
- ServicesANGLE: Hortonworks Announces Partnership with Teradata
Original title and link: Teradata and Hortonworks Partnership and What It Means ( ©myNoSQL)
On the other hand, to address the question in the title—would custom distributions clarify Hadoop versions—I think that while custom distributions might be helpful for experimenting or getting started with Hadoop, long term they’ll actually lead to more segmentation in the market and bigger maintenance and upgrade costs for end users.
There are just a few companies with a track record of maintaining and distributing open source projects—in the Hadoop space these are Cloudera and Hortonworks (nb Hortonworks is supporting the Apache Hadoop distribution). So if a vendor tries to sell you a Hadoop package ask them about their history managing open source distributions.
Original title and link: Apache Hadoop 1.0 Doesn’t Clear Up Trunks and Branches Questions. Do Distributions? ( ©myNoSQL)
Just a quick roundup of the latest releases and announcements.
Hortonworks Data Platform (HDP) version 2
HDP v2 will include:
- NextGen MapReduce architecture
- HDFS NameNode HA
- HDFS Federation
- up-to-date HCatalog, HBase, Hive, Pig
According to the announcement:
In order to avoid confusion, let me explain the two versions of HDP:
- HDP v1 is based upon Apache Hadoop 1.0 (which comes from the 0.20.205 branch). It the most stable, production-ready version of Hadoop that is currently found in many large enterprise deployments. HDP v1 is currently available as a private technology preview. A public technology preview will be made available later this quarter.
- HDP v2 is based upon Apache Hadoop 0.23, which includes the next generation advancements mentioned above. It’s an important step forward in terms of scalability, performance, high availability and data integrity. A technology preview will also be made publicly available later in Q1.
SolrCloud Completes Phase 2
Mark Miller about the completion of phase 2:
The second phase of SolrCloud has been in full swing for a couple of months now and it looks like we are going to be able to commit this work to trunk very soon! In Phase1 we built on top of Solr’s distributed search capabilities and added cluster state, central config, and built-in read side fault tolerance. Phase 2 is even more ambitious and focuses on the write side. We are talking full-blown fault tolerance for reads and writes, near real-time support, real-time GET, true single node durability, optimistic locking, cluster elasticity, improvements to the Phase 1 features, and more.
Not there yet, but it’s coming.
DataStax Community Server 1.0.7
A new release of DataStax’s distribution of Cassandra incorporating Cassandra 1.0.7
Don’t let the version number trick you. This is an important release for HBase featuring:
- new (self-migrating) file format
- AWS improvements: EBS support, building a HA cluster
I’m leaving you with Andrew Purtell’s slides about HBase Coprocessors:
Just a quick recap:
- Cloudera: Oracle, Dell, NetApp
- Hortonworks: Microsoft
- MapR: EMC (integration with Greenplum HD)
Amazon doesn’t partner with anyone for their Amazon Elastic Map Reduce. And IBM is walking alone with the software-only InfoSphere BigInsights.
Original title and link: Partnerships in the Hadoop Market ( ©myNoSQL)