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Database Revolutions, Reloaded By @ABridgwater | @BigDataExpo [#BigData]

The era of the Big Data analytics platforms

Database Revolutions, Reloaded

The evolution of the database is under constant upheaval, discussion, debate and (if you will excuse the expression) 'analysis.'

This basic truth is now more relevant, pertinent and pressing than ever due to the prevalence of Big Data (and the need to impose analytics of insight upon it) driven by social, mobile, cloud and of course the Internet of (Every) Things.

Today then, as a staple of our IT infrastructure, databases have been around for over 50 years now with first references of the term dating back to the very early 1960s. Given that half century of data, it is only really our last thirty years of data history that we need to spend time worrying about.

The three waves of database data
HP has pointed to three major innovation waves in database history.

Starting out with mainframes, we know that these formed the bedrock of the first age of databases - and these were populated and popularized (and so of course refined) by government, the financial services industry, telecommunications - and here's the interesting thing, these were the industries that used huge swathes of data back in the day.

Today it's all high volume business transactions.

Note: As we now know, the number of industries we think of as Big Data users has of course multiplied and mushroomed since the 1960s and we now add retail, pharmaceutical, media, oil and gas and just every other vertical into our typical high volume business transactions user list.

The second age of data was driven by OLTP.

Online Transactional Processing (OLTP) Databases proliferated upwards at the same time the first glory days of client/server computing.

According to an HP white paper, "The client/server model offered affordable computing with the birth of relational, OLTP databases. These databases became even more widely accessible to consumers and suppliers through the Internet in the form of dynamic web applications, CRM/ERP and e-commerce systems."

The third age of man... and data warehouses
We associate the term data warehouses with the third wave of formalized database systems when we saw a new stream of transactional data flowing at us.

These transactional data warehouse systems grew up to serve business functions including HR, sales, finance and those aspects of what we now consider to be the modern connected elements of computing.

Another way of describing this third age of data usage is to call it "operational software for analytical insight" - a piece of terminology that we would have baulked at in the early mainframe years, but one that we find completely normal today.

The operational transactional years
The operational transactional years (for want of a better de facto term) saw the growth of many many database vendors, some of which were new vendors born out of the realization of a new opportunity... and some were extensions of older iterations of old OLTP databases.

Note: At the same time, this era saw an entire industry of BI (Business Intelligence) and ETL (Extract, Transform, Load) tools rise to prominence.

The Fourth Wave
There is fourth wave and this is the era of the Big Data analytics platforms and this is where we could hear vendors like IBM talk about Watson, SAP talk about HANA and HP talk about Vertica.

HP asserts that Big Data analytics has the potential to crest over all preceding waves.

"Market leaders recognize that data is their most strategic asset and offers unique competitive advantage. But technology evolution is not enough. Handling the volume, variety and velocity of Big Data, as Gartner describes it, goes far beyond what traditional data warehouses were ever designed to handle. Today, organizations need to analyze massive volumes of data - structured, semi-structured, and unstructured - at unprecedented speed and with a more advanced analytics approach," said the company, in a recent white paper.

Next time we will look more closely and directly at the HP Vertica Analytics Platform, which its makers say was purpose built from the first line of code for Big Data analytics.

This post is sponsored by The Business Value Exchange and HP Enterprise Services

More Stories By Adrian Bridgwater

Adrian Bridgwater is a freelance journalist and corporate content creation specialist focusing on cross platform software application development as well as all related aspects software engineering, project management and technology as a whole.

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