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.
15 of 17

Pivotal eyes cloud, big data, and app development 
 
Analytical DBMS: Pivotal Greenplum Database. 
In-memory DBMS: Pivotal GemFire and SQLFire. Pivotal HD used in combination with GemFire XD and HAWQ for in-memory analysis on top of Hadoop. 
Stream-analysis option: Pivotal is working a project aimed at integrating its GemFire (NoSQL) and SQLFire in-memory data grid capabilities with Pivotal Hadoop and Spring XD as a data-ingest mechanism to support scalable, streaming-data analysis.  
Hadoop distribution: Pivotal HD.  
Hardware/software systems: Pivotal Data Computing Appliance
There's no shortage of ambition at Pivotal, an EMC spinoff that offers big-data infrastructure as well as an abstraction layer for cloud computing (based on Cloud Foundry) and an agile application development environment (based on SpringSource). Pivotal's big-data and analytics capabilities blend the Pivotal HD Hadoop distribution with GemFire SQL Fire in-memory technology, the Greenplum database, and HAWQ (Hadoop With Query) SQL querying capabilities. It also has close ties and in-database integrations with SAS analytics. 
The question with Pivotal is just how much energy, investment, and 'oomph' it can bring to three bold fronts of next-generation computing: big data, cloud, and application development. Pivotal's largest competitors -- IBM, Oracle, and Microsoft -- can rely on the revenue from well-established data-integration, data-quality, BI, and analytics software that Pivotal lacks. Competitors such as Cloudera, Hortonworks, and Teradata can focus exclusively on big-data analytics. Time will tell if Pivotal's products and execution can keep up with its bold ambitions for big data as well as cloud integration and application development.

Pivotal eyes cloud, big data, and app development

Analytical DBMS: Pivotal Greenplum Database.
In-memory DBMS: Pivotal GemFire and SQLFire. Pivotal HD used in combination with GemFire XD and HAWQ for in-memory analysis on top of Hadoop.
Stream-analysis option: Pivotal is working a project aimed at integrating its GemFire (NoSQL) and SQLFire in-memory data grid capabilities with Pivotal Hadoop and Spring XD as a data-ingest mechanism to support scalable, streaming-data analysis.
Hadoop distribution: Pivotal HD.
Hardware/software systems: Pivotal Data Computing Appliance

There's no shortage of ambition at Pivotal, an EMC spinoff that offers big-data infrastructure as well as an abstraction layer for cloud computing (based on Cloud Foundry) and an agile application development environment (based on SpringSource). Pivotal's big-data and analytics capabilities blend the Pivotal HD Hadoop distribution with GemFire SQL Fire in-memory technology, the Greenplum database, and HAWQ (Hadoop With Query) SQL querying capabilities. It also has close ties and in-database integrations with SAS analytics.

The question with Pivotal is just how much energy, investment, and "oomph" it can bring to three bold fronts of next-generation computing: big data, cloud, and application development. Pivotal's largest competitors -- IBM, Oracle, and Microsoft -- can rely on the revenue from well-established data-integration, data-quality, BI, and analytics software that Pivotal lacks. Competitors such as Cloudera, Hortonworks, and Teradata can focus exclusively on big-data analytics. Time will tell if Pivotal's products and execution can keep up with its bold ambitions for big data as well as cloud integration and application development.

15 of 17
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eyu906
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eyu906,
User Rank: Strategist
1/6/2015 | 12:36:09 PM
Drill-downs?
Dell Boomi is the #1 cloud integration platform.  Are you going to drill down to help users regarding technology strategy?
KenB037
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KenB037,
User Rank: Apprentice
9/24/2014 | 10:27:52 PM
Great article! When is the next update?
Super overview article! I realize that it will be a lot of work, but it would be great if you decide to write an update sometime time soon.  I am already looking forward to it!
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.
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