Twitter LinkedIn Facebook RSS Android

Big Data Buyer's Guide, Part Two: IBM, SAS, Pentaho and More: Page 2

By Drew Robb     Feedback
Previous |   Page 2 of 2   |   1 2

Pentaho is not addressed to a specific database or application, but covers a range of data types, formats and sizes. As such, it encompasses data access and integration, data discovery and analysis, as well as data visualization, reporting and dashboards on Big Data sources.

In November Pentaho released Business Analytics 4.1, software that provides advanced in-memory features that enable enterprises to leverage the benefits of in-memory as well as disk-based analysis. In contrast to a pure in-memory model, Pentaho’s approach is not limited by scalability issues. To optimize performance, companies can choose what data gets loaded into memory and manage the data cache.

Like most Pentaho applications, Business Analytics 4.1 is available in both open source community and commercial editions. A key differentiator is the advanced in-memory integration offered with the commercial release.


Another company that is not as well known as SAS, IBM, SAP or Oracle, Birst  just released version 5 of its business intelligence software, which it has positioned to compete with the Oracle Exalytics and Big Data appliances, as well as HANA.

“Exalytics and HANA are being optimized for analytics by Oracle and SAP to run their business intelligence solutions on top of,” said Wynn White, vice president at Birst. “These guys for years have been piece-mealing the overall solution together from mostly acquired, and in some rarer cases, built parts.” 

Related Articles

He believes this assemble-the-parts approach translates into poor integration, which means costly implementations and an uneven experience for users. By building business intelligence from the ground up, he said, overall deployments can be made faster and cheaper.  

The company calls Birst 5 the first in-memory database optimized exclusively for business analytics.  It uses a SQL-based, columnar database.

 “Data discovery tools are limited by the data sets they can handle and cannot deliver analytics that scale,” said White. “High-volume, high-speed analytical queries can be accomplished using in-memory technology which is the future of business analytics.”

The Birst in-memory analytics database builds on Birst’s data warehouse automation technology, which provides for data integration across data sources such as SAP, Salesforce, operational and financial systems. By offering it as an appliance, the company gives users the option of deploying its tools as a self-contained appliance, in the cloud or on-premise.


The little guys really do like to pick on SAP and Oracle. Mike Allen, vice president of Product Management at Terracotta, decries both as HANA and Exalytics as high-cost products hoping to lock customers into vendor solutions by speeding up long running (and non-real-time) analytic processes. He said both use in-memory processing within the database but don’t create a bridge between the application and the data.

“At the end of the day SAP HANA and Oracle's solutions are still databases - running on dedicated hardware that has to be sized to the problem in hand - that are accessed across a network,” said Allen. “Terracotta BigMemory stores data right where it’s used, in the memory where the application runs. This makes it much faster.”

BigMemory is a software solution that enables users to process terabytes of data in memory. It is being used in areas such as fraud detection, credit card authorizations, trade order processing, billing, call center/e-service, database/mainframe offload, and high volume customer portals/online services.

“BigMemory excels at servicing both transactional and analytical workloads from within a single unified in-memory store,” said Allen. 

Winners and Losers

Clearly, there are many opinions on which Big Data approach is superior. Barry Cousins, senior research analyst at Info-Tech Research Group, agrees with many of the views above that HANA is an in-memory analytics layer for all things SAP. He’s less kind with regard to Exalytics.

“Oracle Exalytics still looks like a competitive reaction based on a bundle of legacy technologies,” he said.   

Cousins, though, is largely positive about IBM InfoSphere BigInsights, noting that it had a long and strong development path resulting in a reasonably mature product release. Therefore, he feels IBM is well-positioned in the overall data/business intelligence/analytics space. He holds a similar opinion of SAS.

“SAS had the most natural evolution to in-memory analytics of the major players and are best positioned in terms of expertise, partners, and platform openness for flanking technologies,” Cousins said. “IBM and SAS stand out as the vendors with the most interesting blend of technology, expertise and partners for clients with complex analytics needs.”

His thoughts on some others:  Birst is aimed at mid-sized companies with a data warehouse /business intelligence solution that integrates well with existing data technologies. Pentaho’s commercial open-source model and subscription pricing have driven faster adoption in non-U.S. markets than their larger competitors.

And don’t count out HP, he said, which acquired Vertica last year and aims to make it central to its Big Data analytics strategy.  In November HP announced Vertica Analytics Platform, software designed to improve real-time performance for big business intelligence and analytic workloads that can analyze data on a massive scale.

Drew Robb is a freelance writer specializing in technology and engineering. Currently living in California, he is originally from Scotland, where he received a degree in geology and geography from the University of Strathclyde. He is the author of Server Disk Management in a Windows Environment (CRC Press).

This article was originally published on February 27, 2012
Previous |   Page 2 of 2   |   1 2
Close Icon
Thanks for your registration, follow us on our social networks to keep up-to-date