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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 warehouse silos. 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)

Happiness of #DevOps Transformation | @DevOpsSummit #APM #ML #SDN

Three Reasons Why DevOps Transformation Produces Happiness By Charlie Coffey "It is not necessary to change. Survival is not mandatory." - W. Edwards Deming. 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? | @DevOpsSummit #Agile #DevOps #DigitalTransformation

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 With DevOps changing the way digital apps are built, traditional "waterfall" methodologies look clunky and outdated.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)

Difference Between a Data Lake and a Data Warehouse | @BigDataExpo #BigData #DataLake #Storage

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)

Docker Containers and #Microservices | @DevOpsSummit #DevOps #Docker

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 'containerology'. 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)