Cloudera: All content tagged as Cloudera in NoSQL databases and polyglot persistence
A couple of links covering various aspects of this question:
- Quora thread covering this subject
- Joe Stein’s Hadoop distribution bake-off and my experience with Cloudera and MapR
- How I’d choose a Hadoop distribution
- MapR claims title as de facto standard for Hadoop
If you have other good references answering the question of what Hadoop distribution to choose please leave a comment.
Original title and link: Cloudera or MapR for Hadoop Distribution? ( ©myNoSQL)
Found the following bits in a post on The Register by Timothy Prickett Morgan:
While Cloudera and MapR are charging $4,000 per node for their enterprise-class Hadoop distributions (including their proprietary extensions and tech support), Hortonworks doesn’t have any proprietary extensions and is living off of the support contracts for the HDP 1.0 stack. […] Hortonworks is not providing its full list price, but for a starter ten-node cluster, you can get a standard support contract for $12,000 per year.
Hortonworks’s pricing looks a bit aggressive, but this could be explained by the fact that Hortonworks Data Platform 1.0 was made available only this week.
For running Hadoop in the cloud, there’s also Amazon Elastic MapReduce whose pricing was always clear. And Amazon has recently announced support for MapR Hadoop distribution on Elastic MapReduce.
Original title and link: Pricing for Hadoop Support: Cloudera, Hortonworks, MapR ( ©myNoSQL)
What Are the Pros and Cons of Running Cloudera’s Distribution for Hadoop vs Amazon Elastic MapReduce Service?
Old Quora question, but still very relevant. Top response from Jeff Hammerbacher:
Elastic MapReduce Pros:
- Dynamic MapReduce cluster sizing.
- Ease of use for simple jobs via their proprietary web console.
- Great documentation.
- Integrates nicely with other Amazon Web Services.
Cloudera Distribution for Hadoop:
- CDH is open source; you have access to the source code and can inspect it for debugging purposes and make modifications as required.
- CDH can be run on a number of public or private clouds using an open source framework, Whirr, so you’re not tied to a single cloud provider
- With CDH, you can move your cluster to dedicated hardware with little disruption when the economics make sense. Most non-trivial applications will benefit from this move.
- CDH packages a number of open source projects that are not included with EMR: Sqoop, Flume, HBase, Oozie, ZooKeeper, Avro, and Hue. You have access to the complete platform composed of data collection, storage, and processing tools.
- CDH packages a number of critical bug fixes and features and the most recent stable releases, so you’re usually using a more stable and feature-rich product.
- You can purchase support and management tools for CDH via Cloudera Enterprise.
- CDH uses the open source Oozie framework for workflow management. EMR implemented a proprietary “job flow” system before major Hadoop users standardized on Oozie for workload management.
- CDH uses the open source Hue framework for its user interface. If you require new features from your web interface, you can easily implement them using the Hue SDK.
- CDH includes a number of integrations with other software components of the data management stack, including Talend, Informatica, Netezza, Teradata, Greenplum, Microstrategy, and others. […]
- CDH has been designed and deployed in common Linux environments and you can use standard tools to debug your programs. […]
Make sure you also read Hadoop in the Cloud: Pros and Cons which addresses (almost) the same question.
A Twitter-style answer to this question would be: “Control and customization vs Automated and Managed Service”. 80 characters left to add your own perspective.
Original title and link: What Are the Pros and Cons of Running Cloudera’s Distribution for Hadoop vs Amazon Elastic MapReduce Service? ( ©myNoSQL)
Very interesting visualization of some of the companies in the Big Data market connected through their venture capital and investment firms by Benedikt Koehler and Joerg Blumtritt over Beautiful Data blog:
There’s only one company I couldn’t find on this map: Hortonworks.
Original title and link: Big Data Investment Network Map ( ©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)
My list of 8 most interesting companies for the future of Hadoop didn’t try to include anyone having a product with the Hadoop word in it. But the list from InformationWeek does. To save you 15 clicks, here’s their list:
- Amazon Elastic MapReduce
- EMC (with EMC Greenplum Unified Analytics Platform and EMC Data Computing Appliance)
- IBM (InfoSphere BigInsights)
- Informatica (for HParser)
Original title and link: 12 Hadoop Vendors to Watch in 2012 ( ©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 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)