Five Key Requirements for Enabling Agile Analytics
By Yann Guernion
In today's digital economy, companies are faced with a fast data challenge as
well as a Big Data one. As a result they are under pressure to adapt their
analytics processes and data flows at pace to move beyond traditional data
Big Data projects are either too big or too complex to handle the traditional
way. That's why most projects by companies at the start of their Big Data
initiative have no process at all. Waterfall approaches are notably
inefficient as you probably won't have access to proper staging environment
and only limited time and scale for qualification.
Big Data and DevOps
Big Data implicitly promotes DevOps because there is no ability to separate
Operations from Development when you ultimately discover the relevance of
your algorithms at the production stage. It is in... (more)
Three Reasons Why DevOps Transformation Produces Happiness
By Charlie Coffey
"It is not necessary to change. Survival is not mandatory." - W.
How often do we see this quote used in DevOps blogs without a hint of irony?
It's as if we need to instantly complete generations of evolution to stave
off extinction, like trying to grow an extra lung overnight.
DevOps or Die!!!
So this is it - the dreaded DevOps transformation looms large. The department
will be ‘shaken up', practices will be ‘turned on their head', and staff
will be ‘taken out of their comfort zone'. It's... (more)
What Is Bimodal IT?
By Courtney Glymph
Gartner's concept of Bimodal IT argues that for successful digital
transformation, IT needs to split into two parts: mode 1 for maintaining and
modernizing traditional back-end IT services and mode 2 for agility in
building front-end, digital apps. This allows IT to respond to the digital
divide emerging in their organizations by operating in two coherent but
deeply different modes.
With DevOps changing the way digital apps are built, traditional "waterfall"
methodologies look clunky and outdated. However, rather than making
old-school, st... (more)
What Is the Difference Between a Data Lake and a Data Warehouse?
By Dave Kellermanns
The data warehouse and data lake are two different types of data storage
repository. The data warehouse integrates data from different sources and
suits business reporting. The data lake stores raw structured and
unstructured data in whatever form the data source provides. It does not
require prior knowledge of the analyses you think you want to perform.
What is a Data Lake?
A data lake is a storage repository that holds a vast amount of raw data in
its native format until it is needed. While a hie... (more)
A History of Docker Containers and the Birth of Microservices
by Scott Willson
From the conception of Docker containers to the unfolding microservices
revolution we see today, here is a brief history of what I like to call
In 2013, we were solidly in the monolithic application era. I had noticed
that a growing amount of effort was going into deploying and configuring
applications. As applications had grown in complexity and interdependency
over the years, the effort to install and configure them was becoming
significant. But the road did not end with a single d... (more)