Big Data // Big Data Analytics
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1/30/2014
09:06 AM
Doug Henschen
Doug Henschen
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16 Top Big Data Analytics Platforms

Data analysis is a do-or-die requirement for today's businesses. We analyze notable vendor choices, from Hadoop upstarts to traditional database players.
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Revolutionary. That pretty much describes the data analysis time in which we live. Businesses grapple with huge quantities and varieties of data on one hand, and ever-faster expectations for analysis on the other. The vendor community is responding by providing highly distributed architectures and new levels of memory and processing power. Upstarts also exploit the open-source licensing model, which is not new, but is increasingly accepted and even sought out by data-management professionals.

Apache Hadoop, a nine-year-old open-source data-processing platform first used by Internet giants including Yahoo and Facebook, leads the big-data revolution. Cloudera introduced commercial support for enterprises in 2008, and MapR and Hortonworks piled on in 2009 and 2011, respectively. Among data-management incumbents, IBM and EMC-spinout Pivotal each has introduced its own Hadoop distribution. Microsoft and Teradata offer complementary software and first-line support for Hortonworks' platform. Oracle resells and supports Cloudera, while HP, SAP, and others act more like Switzerland, working with multiple Hadoop software providers.

In-memory analysis gains steam as Moore's Law brings us faster, more affordable, and more-memory-rich processors. SAP has been the biggest champion of the in-memory approach with its Hana platform, but Microsoft and Oracle are now poised to introduce in-memory options for their flagship databases. Focused analytical database vendors including Actian, HP Vertica, and Teradata have introduced options for high-RAM-to-disk ratios, along with tools to place specific data into memory for ultra-fast analysis.

Advances in bandwidth, memory, and processing power also have improved real-time stream-processing and stream-analysis capabilities, but this technology has yet to see broad adoption. Several vendors here complex event processing, but outside of the financial trading, national intelligence, and security communities, deployments have been rare. Watch this space and, particularly, new open source options as breakthrough applications in ad delivery, content personalization, logistics, and other areas push broader adoption.

Our slideshow includes broad-based data-management vendors -- IBM, Microsoft, Oracle, SAP -- that offer everything from data-integration software and database-management systems (DBMSs) to business intelligence and analytics software, to in-memory, stream-processing, and Hadoop options. Teradata is a blue chip focused more narrowly on data management, and like Pivotal, it has close ties with analytics market leader SAS.

Plenty of vendors covered here offer cloud options, but 1010data and Amazon Web Services (AWS) have staked their entire businesses on the cloud model. Amazon has the broadest selection of products of the two, and it's an obvious choice for those running big workloads and storing lots of data on the AWS platform. 1010data has a highly scalable database service and supporting information-management, BI, and analytics capabilities that are served up private-cloud style.

The jury is still out on whether Hadoop will become as indispensable as database management systems. Where volume and variety are extreme, Hadoop has proven its utility and cost advantages. Cloudera, Hortonworks, and MapR are doing everything they can to move Hadoop beyond high-scale storage and MapReduce processing into the world of analytics.

The niche vendors here include Actian, InfiniDB/Calpont, HP Vertica, Infobright, and Kognitio, all of which have centered their big-data stories around database management systems focused entirely on analytics rather than transaction processing. German DBMS vendor Exasol is another niche player in this mold, but we don't cover it here as its customer base is almost entirely in continental Europe. It opened offices in the U.S. and U.K. in January 2014.

This collection does not cover analytics vendors, such as Alpine Data Labs, Revolution Analytics, and SAS. These vendors invariably work in conjunction with platforms provided by third-party DBMS vendors and Hadoop distributors, although SAS in particular is blurring this line with growing support for SAS-managed in-memory data grids and Hadoop environments. We also excluded NoSQL and NewSQL DBMSs, which are heavily (though not entirely) focused on high-scale transaction processing, not analytics. We plan to cover NoSQL and NewSQL platforms in a separate, soon-to-be-published collection.

Now dig in and learn more about these analytics vendors and how they compare.

 

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LesterK048
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LesterK048,
User Rank: Apprentice
8/8/2014 | 2:51:40 AM
Re: It's time for this update
A smaller company which can process big JSON data for easier visualization is json-csv.com. You may want to check it out.
bigdatarelated
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bigdatarelated,
User Rank: Apprentice
4/23/2014 | 11:24:38 AM
Re: A collection of marketing flyers from 16 vendors
Great article. I've added a link to it from  Bigdatarelated, a free big data community resource website.
Akon786
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Akon786,
User Rank: Apprentice
2/20/2014 | 6:39:55 AM
Bedrock Data Management Platform 2.0
Comprehensive and well rounded article.

Where does Bedrock Data Management Platform 2.0 figure in the game?
D. Henschen
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D. Henschen,
User Rank: Author
2/11/2014 | 1:28:26 PM
Re: Bravo
Thanks, Wayne. Coming from such an esteemed expert, I'm flattered.
weckerson
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weckerson,
User Rank: Apprentice
2/6/2014 | 4:33:06 PM
Bravo
Doug, 

Well done. This is a ton of work and well done! A great resource. 

 

Wayne
D. Henschen
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D. Henschen,
User Rank: Author
2/5/2014 | 9:18:53 AM
Re: What about Personalized Big Data Analytics?
Analytics tools and BI systems run on servers, but these systems are generally not scaled to handle big data. More often than not, these systems draw data from data warehouses or data marts. Increasingly, a larger-scale "platform" such as a massively parallel processing (MPP) database management system or Hadoop cluster is required to handle the volume and variety of data. Some analytics vendors, notably SAS but including others, are developing their own in-memory cluster software or implementations on top of Hadoop, but the vast majority of clients use analytics and BI software in combination with data-management platforms from third-party vendors like those covered in the collection above.

Confusing matters, many vendors above offer analytic capabilites -- IBM has SPSS and Cognos; SAP has BusinessObjects and Predictive Analysis; Oracle, Pivotal, and Teradata tap advanced SQL analytics, R and various partnerships with analytics vendors including SAS, etc. -- but they're not included in this collection because of those capabilites.

There are many options for smaller companies -- including cloud, price-competitive upstart vendors, and open source options. But where this is great data volume, variety, and velocity, there's a need for a high-scale platform or platforms to serve as the place where the analysis gets done (as with in-database or in-Hadoop analytics) or as the place from which subsets of data are drawn or analyzed (as in the case of Hadoop or data warehouse integration).

 
CFree22
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CFree22,
User Rank: Apprentice
2/5/2014 | 12:43:38 AM
Re: What about Personalized Big Data Analytics?
I apologize for being confused about this. The title just made it seem like big analytis platforms were going to be highlighted for their top features. So, Jaspersoft and the like are not considered to have big analytics platforms?  Do you think the platforms you metioned are worth the investment for smaller businesses or is that kind of analytics too cost-prohibitive? I think a lot of people are still confused about how big data can be made useful and applied to business analytics in general. 

Thank you for the side by side breakdowns of each platform.
D. Henschen
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D. Henschen,
User Rank: Author
2/4/2014 | 9:17:12 PM
Re: What about Personalized Big Data Analytics?
Once, again, as I've pointed out to others who didn't read the introduction, these are big data anaytics platforms -- the relational databases (for warehouses and marts) and Hadoop platforms that are the underpinning for the vast majority of analytic persuits. As pointed out in the introduction, this is not about pure analytics vendors such as SAS, Alpine Data Labs, Revolution Analytics, the whole R community or, for that matter, more BI-focused vendors such as Actuate, QlikTech, Tableau, MicroStrategy, etc. Nor is it about NoSQL and NewSQL databases, which are predominatly (though not exclusively) used to run high-scale transactional applications.
CFree22
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CFree22,
User Rank: Apprentice
2/4/2014 | 7:24:14 PM
What about Personalized Big Data Analytics?
Like the other comments inquired, how were these determinations made regarding the top 16 Big Data Analytics Platforms? What data was used? (Was it based on scalability factors or the number of companies in an industry that used it?) Why wasn't Actuate included? I find this fascinating especially since the BIRT Analytics software, and BIRT reporting software is used for big data analytics. Hortonworks, Actian, and Amazon web services have partnered with Actuate for big data deployments and they use BIRT technology. Do you have feedback from the business users and the end users comparing their experiences with the platforms? I am just curious what that kind of data looks like. How does the security of the data and the scalability come into play when evaluating these platforms? What about the time it takes to implement the platforms and get everyone trained in using them--was that a factor? What makes some better than others aside from the purpose of their use? I really appreciate articles that provide this kind of side by side comparison, and I would like to see more of it in the future. I wonder how small to medium busineses handle this big kind of technology though. Big enterprises definitely need these big platforms. Thank you for your article. I look forward to reading more. :)
D. Henschen
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D. Henschen,
User Rank: Author
2/3/2014 | 12:47:07 PM
Re: A collection of marketing flyers from 16 vendors
Excellent take, Raj. The likes of IBM, Oracle and Teradata have certainly checked the Hadoop box, but I wonder how hard they push it or whether they try to keep it in a high-scale storage role while favoring their incumbent technologies for the analysis. Cloudera and MapR are saying you can do more and challenge incumbent technologies while Hortoworks holds short of such bold claims -- clearly not wanting to challenge partners Microsoft, Teradata and SAP. The independent DBMS vendors have various strategies and capabilities around working with Hadoop, and they generally don't challenge EDW vendors -- only the high-scale data mart/analytics opportunity. All of these vendors offer "Big Data Analytics Platforms," but they're coming at it from secular angles.
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