Category Archives: Data Services
Seven Essential Best Practices For Data Center Consolidation
Data center consolidation is much more than physical movement of servers and infrastructure. In fact, the facility costs and power savings are just the tip of the opportunity. The biggest benefits come from using the consolidation initiative as a catalyst to rationalize the application portfolio, archive inactive data and establish one version of the truth for the data that is left. (more…)
Hadoop Tuesday Update: Hadoop Paves the Way to Data Services
For too long, many enterprises have been attempting to sort through increasingly complex spaghetti architectures with point-to-point data integration. “They get to the point where when they want to introduce a new product or make a change, they have to touch 30 different systems,” says John Akred, data and platforms lead at Accenture Technology Labs. “That has real consequences in the marketplace for enterprises.”
John continued that Hadoop – an open-source software framework that enables applications to run across large arrays of nodes, accessing petabytes’ worth of data – will help organizations manage and scale up to the huge volumes of unstructured and semi-structured data now surging into organizations. I recently had the opportunity to join John, along with Julianna DeLua, Enterprise Solution Evangelist for Big Data from Informatica, for a discussion of Hadoop’s role in the emerging data as a platform paradigm. The session was the second session of the Hadoop Tuesdays Webinar series, sponsored by Informatica and Cloudera. (more…)
Loose Coupling Nirvana – Canonical Techniques Part 2
To continue from my prior blog article on this topic, loose coupling between applications in an enterprise portfolio is an IT architect’s dream. If two or more applications are tightly coupled, then it becomes impossible to change or enhance one without impacting the other. Loosely coupled applications on the other hand can be enhanced independently with little or no impact on other systems. The net result is the ability to rapidly change the IT portfolio in response to business opportunities. In short, organizational agility becomes a competitive weapon. But is this dream achievable or is it only wishful thinking? (more…)
Agile Data Integration Maximizes Business Value In The Era Of Big Data
Adopting Agile may require a cultural shift and in the beginning can be disruptive to an organization. However, as I mentioned in Part 1 of this blog series, Agile Data Integration holds the promise to increase chances of success, deliver projects faster, and reduce defects. Applying Lean principles within your organization can help ease the transition to Agile Data Integration. Lean is a set of principles first explored in the context of data integration by John Schmidt and David Lyle in their book on Lean Integration. First and foremost Lean recommends an organization focus on eliminating waste and optimizing the data integration process from the customers’ perspective. Agile Data Integration maximizes the business value of projects (e.g. Agile BI, Data Warehousing, Big Data Analytics, Data Migration, etc.) because you can get it right the first time by delivering exactly what the business needs when they need it. Break big projects into smaller more manageable deliverables so that you can incrementally deliver value to the business. Agile Data Integration also recommends the following: (more…)
Big Data Have You Flummoxed? Join Our ‘Hadoop Tuesdays’ Webinar Series, Starting This Month
This fall, I have the fantastic privilege of moderating a series of informative Webcasts, called “Hadoop Tuesdays,” co-sponsored by Informatica and Cloudera, on the phenomenon sweeping the data management space known as Hadoop.
Big Data may be the problem, but Hadoop is the answer. Hadoop is an open-source software framework that enables applications to run across large arrays of nodes, accessing petabytes’ worth of data. It was originally created by Doug Cutting to support the open-source Nutch search engine project, which is now part of the Apache Lucene text-search library. ‘Hadoop’ was actually named after Cutting’s son’s toy elephant – a fitting analogy for the Big Data challenges that lie ahead.
The series kicks off on September 22nd with a “TweetJam” over the Twitter network – simply check in at Noon Eastern Time that day with hashtags #Hadoop or #infatj. (more…)
Four Canonical Techniques That Really Work (Or Not)
Several years ago I had the fortunate opportunity to participate in a post-mortem study of a $100 million dollar project failure. No one likes to be associated with a project failure, but in this case it was fortunate since the size of the write-off was large enough that it forced the team to take a very hard look at root causes and not just do a cursory analysis. As a result we finally got to the heart of a challenge that has been plaguing data architects and designers for 20 years – how to effectively use canonical data models. (more…)
More On The Value Of SOA Data Services
Data services, data services, data services. Do I sound like a broken record? Forgive me if I seem obsessed with the topic, but I truly believe that technology can change your enterprise, and allow IT to finally get a handle on data in the shortest amount of time.
The real value lies in SOA data services. These services allow enterprises to place an easy-to-configure layer between the source physical databases and those that wish to consume the data, either applications or humans. If this seems simple, why, you are right! It is. Why is it so simple? It’s because the complexity is hidden from you, including the access mechanisms to the physical data, the transformation of schemas from physical to abstract, and even the management of data quality and integrity.
So where is the value? There are three core points to consider here: (more…)
Even More On Big Data And Data Integration
I was on the big data bandwagon before everyone began to jump on. The value is very clear. Simply put, it’s the ability to manage terabytes and terabytes of data as if it were just a small data set.
Big data is possible. We take a divide-and-conquer approach to processing queries and other data operations. The operations on data are divided up on many different servers, perhaps thousands of times, and then the results are recombined later when all of the operations are complete. This is technology seen in the most popular big data technology, Hadoop leveraging a map-reduce approach to manage data and operations on data. The larger database guys are picking up on this trend and moving in this direction as well. (more…)
Big Data Unleashed Part 3: Five Considerations For Your Information Management Agenda With Big Data
In Part 2 of the “Big Data Unleashed” series, I discussed how business experimentation and curiosity are at the heart of business enthusiasm for Big Data. Today, I will discuss how organizations are responding to this Big Data excitement by accelerating the quest for becoming data-centric ─ revisiting and updating their information management roadmaps. (more…)
Does your Data Virtualization Solution Adapt To (Big) Data Problems
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…)
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