Inside Analysis

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New Directions in Analytics: How a Modern Rapid Analytics Platform Enables the Business

by Robin Bloor, Ph.D. and Rebecca Jozwiak

Historically, software development products tread a natural path of evolution. Initially, point products emerge that deliver useful development capability, and then heterogenous tool sets develop that provide broad coverage of the application area. Next, you see integrated platforms that thread together the complementary components in a well-integrated way. Finally, we tend to see rapid application development introduced. We witnessed such software evolutions following the birth of relational database in the 1980s and again with the advent of data warehouses and BI. We see it playing out now with analytics.

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Exaptive offers a rapid application development platform for data science.

For more information visit: http://www.exaptive.com/


The Agile Mainframe: Compuware Re-Engergizes the Mainframe

by Robin Bloor, Ph.D. and Rebecca Jozwiak

While the likes of such companies as Google and Facebook build upon the power of distributed computing, large corporations know that some tasks simply run better on mainframes. Organizations with high volumes of transactions – banks, telcos and retailers – have for decades trusted the mainframe for its impeccable performance and reliability with transaction processing. The applications running on mainframes support the business and create value, and just as technology has evolved to meet modern demands, the workhorse mainframe has evolved to address today’s business needs.    

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Compuware specializes in mainframe modernization, bridging the gap between decades of mainframe development and today’s agile-minded professional.

For more information visit: http://www.compuware.com


A Database Platform for the Internet of Things
by Robin Bloor, Ph.D. & Rebecca Jozwiak

We think of the Internet of Things (IOT) as new and dramatic. And indeed, no doubt its impact will soon be dramatic, but it is not as new as many people suppose. In respect to consumer-facing technology, we can trace the IOT back to the invention of ATMs in the late 1960s, nearly 50 year ago. True, we are pushing the definition of IOT here. The first ATMs were not connected, and their connected use only became widespread in the 1980s – and even then, although it was a connected device, it initially only qualified as an Intranet of Things.

Other such early applications included control systems in semi-automated factories. Sensors were placed at key points on production lines and would monitor activity in order to optimize the speed of the production line. These were the first real-time systems, and they were also implemented in chemical plants and oil refineries. RFID tags also emerged sooner than one might imagine, invented in 1973 and deployed in limited applications soon afterwards. They too became part of the early IOT.

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ParStream offers a complete analytics platform specifically built for the Internet of Things.

For more information visit: www.parstream.com


The Road to Real-Time BI

by Robin Bloor, Ph.D. & Rebecca Jozwiak

IT’s value to the business will be increasingly based on the ability to provide real-time analytics and deliver quality data on time to operational processes regardless of the scale of data volume and sources. Current database tools are relatively capable at handling the use cases they were designed to support for data at rest. A still unsolved issue is data flow bottlenecks that are most often a result of overloaded data transformation tasks. 

Trying to scale using conventional methods – by performing the transformations inside the database – has fast become an alternative that results in throwing good money after bad. Big Data is bigger than Hadoop and sometimes comes faster than Hadoop can handle it. There is real need for a data operations flow machine that provides acceleration to data transformation functions in a streaming data architecture.

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The VelociData platform offers engineered solutions designed to accelerate performance.

For more information visit: www.velocidata.com


Why In-Memory Technology Will Dominate Big Data 

by Robin Bloor, Ph.D.

In-memory analytics is not just about time to value, it is also about volume processing. While this may seem counter-intuitive, there are good reasons why this is so. The fact is that without an analytical engine of the kind we describe here, some analytical activity will not even be attempted because the results will arrive far too late. 

The point is that memory is three orders of magnitude (more than 1,000 times) faster than disk, and an increase in speed of that magnitude does not just open up possibilities for the data analyst, it completely changes the approach to analysis projects.

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Sponsored by Kognitio

Kognitio offers an in-memory analytical platform that employs a massively parallel processing engine.

For more information visit: www.kognitio.com

 


Information Management: When One Vendor is Enough 

by Robin Bloor, Ph.D.

When someone says, “I work in IT,” it may mean many things, but at a foundational level, it nearly always means that they enable users to access data. For the greater part of the modern information management lifespan, data has resided in some type of relational database. Because of this, information management – or data management – has been based on structured data and has been carried out primarily by trained personel. When bottlenecks occurred, IT fixed them. When data was needed for analysis, IT fetched it. When new business applications were developed, IT coded for them and provisioned or managed the data. And this worked for a while. 

Nevertheless, tremendous technology shifts have occurred causing serious disruption to the IT environment. They are welcome in many ways because they enable new possibilities, but they also involve formidable challenges.

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Sponsored by Dell

Dell Software offers a comprehensive information management software portfolio that addresses both IT and business needs.

For more information visit: www.dell.com

 


The Phenomenal Speed of In-Chip Analytics

by Robin Bloor, Ph.D.

In recent times a clutch of IT vendors in the BI market have delivered in-memory products that focus on exploiting memory (RAM) as a data store. You may have noted from press releases that reading data from RAM is more than 3,000 times faster than reading it from disk. As a consequence, software designed to exploit that speed difference executes faster. 

Nevertheless, memory is expensive and the data volumes accessed by BI products are often too large to fit in memory. To get the best speed advantage from an in-memory approach, the software needs to cram as much data as possible into memory and the computer needs to have  as much memory as possible. There are complications.

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Sponsored by SiSense

SiSense Prism leverages in-chip technology to deliver high-speed business intelligence.

For more information visit: www.sisense.com