Articles

Adopting DataOps for Agile Data Management Processes

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As businesses become AI-ready, efficient data management has acquired an unprecedented role in ensuring their success. Bottlenecks in the data pipeline can cause massive revenue loss while having a negative impact on reputation and brand value. Consequently, there’s a growing need for agility and resilience in data preparation, analysis, and implementation.

On the one hand, data-analytics teams extract value from incoming data, preparing and organizing it for the production cycle. On the other, they facilitate feedback loops that enable continuous integration and deployment (CI/CD) of new ideas.

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Lesson Learned on Groovy

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Though I have worked on Java for more than a decade, I have not had a chance to work on Groovy. While working for API Integration into Jenkins CI/CD pipeline, I extensively used Groovy to invoke REST API, validate the user input parameters, and business logic for that. After that, I found that Groovy is a fascinating program language for Java developers.

Why Is Groovy Easy for Java Developers?

It allows to use the Java syntax liberally and tries to be as natural as possible for Java developers. It is an object-oriented dynamic programming language for Java virtual machine (JVM) and can be integrated smoothly with any Java Program. The groovy syntax is lucid, familiar, and direct that makes to develop projects faster and easier. It demands a shorter learning curve for Java Developer to develop, test, and integrate to make production-ready code in a short span.

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How to Do Deep Learning for Java

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Deep in thought studying deep learning for Java.

Introduction

Some time ago, I came across this life-cycle management tool (or cloud service) called Valohai, and I was quite impressed by its user-interface and simplicity of design and layout. I had a good chat about the service at that time with one of the members of Valohai and was given a demo. Previous to that, I had written a simple pipeline using GNU Parallel, JavaScript, Python, and Bash — and another one purely using GNU Parallel and Bash.

I also thought about replacing the moving parts with ready-to-use task/workflow management tools like Jenkins X, Jenkins Pipeline, Concourse or Airflow, but due to various reasons, I did not proceed with the idea.

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Could Containers Help You Deliver Software Faster?

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Containers, led by Docker, burst onto the IT scene in 2013. In the five-plus years since, container technology has reshaped the software development landscape. The shift began with a breakdown of applications into lightweight, independent, and deployable software packages.

Since each container includes everything it needs – code, runtime, system tools, and settings, plus all dependencies, libraries, and other configuration files – you can reliably move containers from one environment to another. For IT teams, this reduces frustration when problems occur due to changes between environments. It also makes the process more efficient, which benefits companies and customers.

Source de l’article sur DZONE

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