In Data Engineer’s Lunch #20: DataOps vs DevOps, we discuss the definitions and differences between DataOps and DevOps. The live recording of the Data Engineer’s Lunch, which includes a more in-depth discussion, is also embedded below in case you were not able to attend live. If you would like to attend Data Engineer’s Lunch in person, it is hosted every Monday at 12 PM EST. Register here now![Read more…] about Data Engineer’s Lunch #20: DataOps vs DevOps
This series covers different aspects of architecting and managing a global data & analytics platform. This is not as simple as choosing some technology and installing it. This work involves proper coordination of people, processes, information, and systems to ensure that the business needs are met at all times. We will cover the components of the “SMACK” stack although many people may not necessarily use Akka or Mesos, they will find much value in our coverage of Cassandra, Spark, and Kafka. We will also cover the Anant “STACK” set of procedures which we use at our company to manage data & analytics platforms for our clients.[Read more…] about Architecting & Managing a Global Data & Analytics Platform Part 1: Foundation of a Business Data, Computing, & Communication Framework
Learning a new skill or trade is not always an easy task, especially when you’re relatively new to the workforce. Since joining Anant, I’ve spent a lot of time expanding my skill set to put myself in a position to effectively contribute to the success of my company. One of the skills I’ve been focusing on is Apache Cassandra.
Over 75 percent of the companies polled in a recent survey claimed that they were either in the process of adopting development operations (“DevOps”) practices or had already adopted them. Many companies are using the data they receive to optimize various programs and apps for mass consumption.
The key to beating out the competition in the modern world of business is getting your app to market first. This is where having a knowledgeable and productive DevOps team comes in handy.
Spark,Mesos, Akka, Cassandra, Kafka, Kubernetes? If you don’t already know what these mean and you have no goal or objective to make software that works at a global level, then you don’t need to be reading this article at all. Seriously, it’ll be a waste of your time. These technologies, now open sourced, originated from the extremely high-end university research laboratories of the University of Berkeley and the halls of high-tech companies such as Google, Twitter, LinkedIn, and Facebook. They were built for different purposes for their creators but now being available to the public, they have been flourishing on their own in the wild ether of the Internet. Why would any CIO, CTO, CMO, or a CEO consider these technologies?