Weighed down by what has gone before, and what is needed to keep the lights on, the CIOs at many organizations I have worked with have turned to Hadoop with the hope of utilizing it as a major component of an IT infrastructure and as part of their modernization and migration program for analytics and BI.
In my previous posts, I explored the world of traditional IT as it relates to Hadoop for those CIOs. We have looked at how Hadoop can be deployed without throwing away your warehouse and put forward some approaches people are taking around the data lake concept. All of these are generally focused on finding economically more viable approaches to what we expect to come in the future. If you like, these organizations are focused on improving what they do today while driving down costs.
In parallel to this, two questions have come up time and time again as I have worked with established organizations, of all sizes, over the past 6-12 months. Those questions are:
“How can we, with all their legacy technology constraints, hard to change processes and need to focus on cost control, possibly enable all our business units to compete with nimble competitors that are starting to cast a shadow over many parts of our business?”
“How can we challenge the age old perceptions and approaches of IT, in order to support the business in getting answers to their questions?”
All hail the data lake, destroyer of enterprise data warehouses and the solution to all our enterprise data access problems! Ok – well, maybe not. In part four of this series I want to talk about the confusion in the market I am seeing around the data lake phrase, including a look at how the term seems to be evolving within organizations based on my recent interactions.
In the world of IT, very few new technologies emerge that are not built on what came before, combined with a new, emerging need or idea. The history of Hadoop is no exception.
To understand how Hadoop came to be, we therefore need to understand what went before Hadoop that led to its creation. To understand why Hadoop stagnated for a few years we need to understand how it was initially used. To understand why Hadoop is now accelerating in its adoption, we need to look at what is happening now and where we are headed.
Looking back at the phases of evolution that led to the emergence and incubation of Hadoop along with the current and future path of the technology can help us understand why it has gained in importance and where the hype is coming from.
Weighed down by what has gone before, and what is needed to keep the lights on, the CIOs at many organizations I have worked with have turned to Hadoop with the hope of utilizing it as a major component of an IT infrastructure and as part of their modernization and migration program for analytics and BI.
