16 Top Big Data Analytics Platforms - InformationWeek

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Data Management // 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.
4 of 17

Amazon does it all in cloud 
 
Analytical DBMS: Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service. 
In-memory DBMS: None. Third-party options on AWS include Altibase, SAP Hana, and ScaleOut. 
Hadoop distributions: Amazon Elastic MapReduce. Third-party options include Cloudera and MapR.  
Stream-processing technology: Amazon Kinesis. 
Hardware/software systems: Not applicable.
Amazon Web Services hosts a who's who list of data-management services from third-party players -- Cloudera, Microsoft, Oracle, SAP, and many others -- but the cloud giant has its own long-term ambitions where big-data analysis is concerned. Building on its Elastic Compute Cloud (EC2) and Simple Storage Service (S3) storage infrastructure, Amazon launched its Hadoop-based Elastic MapReduce service way back in 2009. In 2013, AWS added the Redshift Data Warehousing service (based on the ParAccel DBMS), which is supported by another who's who list of independent data-integration, business intelligence, and analytics vendors. Rounding out AWS's big-data capabilities are the DynamoDB NoSQL database management service and Kinesis Stream Processing service.
Amazon's biggest appeal is clearly to organizations running data-intensive applications on its cloud. That said, leading Hadoop distributor Cloudera recently partnered with AWS, apparently reasoning that many enterprises are choosing hybrid strategies in which they're moving selected big-data workloads into the cloud while keeping sensitive data and mission-critical workloads on-premises. Look for AWS to exploit this opportunity by adding even more options to connect with enterprise data centers.

Amazon does it all in cloud

Analytical DBMS: Amazon Redshift service (based on ParAccel engine); Amazon Relational Database Service.
In-memory DBMS: None. Third-party options on AWS include Altibase, SAP Hana, and ScaleOut.
Hadoop distributions: Amazon Elastic MapReduce. Third-party options include Cloudera and MapR.
Stream-processing technology: Amazon Kinesis.
Hardware/software systems: Not applicable.

Amazon Web Services hosts a who's who list of data-management services from third-party players -- Cloudera, Microsoft, Oracle, SAP, and many others -- but the cloud giant has its own long-term ambitions where big-data analysis is concerned. Building on its Elastic Compute Cloud (EC2) and Simple Storage Service (S3) storage infrastructure, Amazon launched its Hadoop-based Elastic MapReduce service way back in 2009. In 2013, AWS added the Redshift Data Warehousing service (based on the ParAccel DBMS), which is supported by another who's who list of independent data-integration, business intelligence, and analytics vendors. Rounding out AWS's big-data capabilities are the DynamoDB NoSQL database management service and Kinesis Stream Processing service.

Amazon's biggest appeal is clearly to organizations running data-intensive applications on its cloud. That said, leading Hadoop distributor Cloudera recently partnered with AWS, apparently reasoning that many enterprises are choosing hybrid strategies in which they're moving selected big-data workloads into the cloud while keeping sensitive data and mission-critical workloads on-premises. Look for AWS to exploit this opportunity by adding even more options to connect with enterprise data centers.

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shane88
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shane88,
User Rank: Apprentice
10/9/2017 | 5:35:16 AM
Re: It's time for this update
This tool might also help: JSON formatter. Have a nice day!
UrvashiS073
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UrvashiS073,
User Rank: Apprentice
4/27/2017 | 1:30:45 PM
IRI Voracity

I would add to this list IRI Voracity, the big data discovery, integration, migration, governance, and analytics platform introduced in 2016 to address the performance, security, quality, complexity and cost issues in legacy vendor and speciality tools.

eyu906
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50%
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
100%
0%
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
0%
100%
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
50%
50%
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
50%
50%
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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50%
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
50%
50%
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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0%
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).

 
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