Tag Archives: ” “SOA
We discussed Big Data and Big Data integration last month, but the rise of Big Data and the systemic use of data integration approaches and technology continues to be a source of confusion. As with any evolution of technology, assumptions are being made that could get many enterprises into a great deal of trouble as they move to Big Data.
Case in point: The rise of big data gave many people the impression that data integration is not needed when implementing big data technology. The notion is, if we consolidate all of the data into a single cluster of servers, than the integration is systemic to the solution. Not the case.
As you may recall, we made many of the same mistakes around the rise of service oriented architecture (SOA). Don’t let history repeat itself with the rise of cloud computing. Data integration, if anything, becomes more important as new technology is layered within the enterprise.
Hadoop’s storage approach leverages a distributed file system that maps data wherever it sits in a cluster. This means that massive amounts of data reside in these clusters, and you can map and remap the data to any number of structures. Moreover, you’re able to work with both structured and unstructured data.
As covered in a recent Read Write article, the movement to Big Data does indeed come with built-in business value. “Hadoop, then, allows companies to store data much more cheaply. How much more cheaply? In 2012, Rainstor estimated that running a 75-node, 300TB Hadoop cluster would cost $1.05 million over three years. In 2008, Oracle sold a database with a little over half the storage (168TB) for $2.33 million – and that’s not including operating costs. Throw in the salary of an Oracle admin at around $95,000 per year, and you’re talking an operational cost of $2.62 million over three years – 2.5 times the cost, for just over half of the storage capacity.”
Thus, if these data points are indeed correct, Hadoop clearly enables companies to hold all of their data on a single cluster of servers. Moreover, this data really has no fixed structure. “Fixed assumptions don’t need to be made in advance. All data becomes equal and equally available, so business scenarios can be run with raw data at any time as needed, without limitation or assumption.”
While this process may look like data integration to some, the heavy lifting around supplying these clusters with data is always a data integration solution, leveraging the right enabling technology. Indeed, consider what’s required around the movement to Big Data systems additional stress and you’ll realize why strain is placed upon the data integration solution. A Big Data strategy that leverages Big Data technology increases, not decreases, the need for a solid data integration strategy and a sound data integration technology solution.
Big Data is a killer application that most enterprises should at least consider. The business strategic benefits are crystal clear, and the movement around finally being able to see and analyze all of your business data in real time is underway for most of the Global 2000 and the government. However, you won’t achieve these objectives without a sound approach to data integration, and a solid plan to leverage the right data integration technology.
Today, agility and timely visibility are critical to the business. No wonder CIO.com, states that business intelligence (BI) will be the top technology priority for CIOs in 2012. However, is your data architecture agile enough to handle these exacting demands?
In his blog Top 10 Business Intelligence Predictions For 2012, Boris Evelson of Forrester Research, Inc., states that traditional BI approaches often fall short for the two following reasons (among many others):
- BI hasn’t fully empowered information workers, who still largely depend on IT
- BI platforms, tools and applications aren’t agile enough (more…)
If you haven’t already, I think you should read The Forrester Wave™: Data Virtualization, Q1 2012. For several reasons – one, to truly understand the space, and two, to understand the critical capabilities required to be a solution that solves real data integration problems.
At the very outset, let’s clearly define Data Virtualization. Simply put, Data Virtualization is foundational to Data Integration. It enables fast and direct access to the critical data and reports that the business needs and trusts. It is not to be confused with simple, traditional Data Federation. Instead, think of it as a superset which must complement existing data architectures to support BI agility, MDM and SOA. (more…)
Late last year at a company all-hands meeting, the CEO of a large consumer electronics company had a serious mandate. He needed big results and fast.
“Competition is really heating up and customer churn is on the rise. I need visibility on-demand – a common view of all enterprise data or else we cannot continue to grow.” IT teams across the enterprise have been scurrying and working furiously to create a common view of CUSTOMER, PRODUCT, SALES, and INVENTORY, but the results have been incomplete, inaccurate and too slow. This is no easy task. (more…)
So, where have I been since my last blog? Well, I have been working on our new Architect to Architect webinar series on data virtualization, which is very exciting for me as I get to rub shoulders (virtually speaking) with hundreds of industry architects.
The interactive nature and record attendance at these webinars have made one thing very clear – data virtualization is indeed top of mind. In my last blog we discussed the concept and how data virtualization is different or a superset of traditional data federation, especially as it overcomes many limitations of the latter. Wayne Eckerson did a great job at tracking the evolution of data federation in a recent webinar and blog. (more…)
We have all heard of data federation and of late we have also been hearing how simple, traditional data federation often gets passed off as data virtualization. Let’s get back to basics and take a hard look at what the real need is.
Data federation is not a new concept. When it first arrived on the scene many years ago, technologists got excited as it offered a way to quickly access numerous disparate data sources without physically moving data. Years passed and the term kept appearing in research paper after research paper – but what did not happen was the anticipated widespread adoption. TDWI’s Wayne Eckerson does a great job at tracking the evolution of data federation in his recent webinar and blog. Simple, traditional data federation does one thing and only one thing well – it creates a virtual view across heterogeneous data sources, delivering data in real-time, typically to reporting tools and composite applications. In its very simplicity lay its downfall.
The “Business” Needs Critical Data “Now” – We Need The Next Generation Data Federation Technology “Yesterday!”
There is a lot of talk about using data federation, Enterprise Information Integration (EII) or data virtualization to deliver new data to the business, on-demand. However, do existing approaches cut it?
I have been following the data integration space for many years now, and like many of you, I have wondered about the viability of data federation as a data integration approach. Not because it does not hold promise – it does – it has many advantages as a fast, flexible and low cost approach to integrate multiple and diverse data sources in real-time, without the need for physical data movement.
However, according to the numerous architects that I have had the pleasure of meeting with on the Informatica 9 World Tour, simple or traditional data federation has not been able to live up to its immense promise. And why is that I asked – the reasons were many…