Gartner has recently released its 2019 Market Guide for Data Preparation ([1]), its fourth edition of a guide that was first published in the early days of the market, back in 2015 when Data Preparation was mostly intended to support self-service uses cases.

When the Application Program Interface (API) first came into existence, developers viewed it as a revolutionary approach to creating re-usable software fragments. Instead of creating new code from scratch for every new program, they could now use existing functionality to develop new features. Not only did this decrease the amount of time needed to deploy a program but also meant they could leverage existing code which was already tried and tested.

We are in the era of the information economy. Now, more than ever, companies have the capabilities to optimize their process through the use of data and analytics. While there are endless possibilities to data analysis, there are still challenges with maintaining, integrating, cleaning it to ensure that it will empower the people to take decisions.

DevOps is all the rage right now, and it is only the beginning. In this blog, I’ll cover how to get started with Talend continuous integration, delivery and deployment (CI/CD) on Azure. The first part of the blog will briefly present some basic DevOps and CI/CD concepts. I will then show you how the Talend CI/CD architecture and how it fits in Azure ecosystem with a hands-on example.

I am very excited to introduce Pipeline Designer, a next-generation cloud data integration design environment that enables developers to develop and deploy data pipelines in minutes, design seamlessly across batch and streaming use cases, and scale natively with the latest hybrid and multi-cloud technologies.

By now, you know that data is the lifeblood of digital transformation. But the true digital leaders have taken a step beyond by starting to understand the need to preserve this lifeblood with people, process and tools. That’s why data quality is so important in its ability to take control of the health of your data assets from diagnostic to treatment and monitoring with whistleblowers.

Let me start by thanking all those who read the first part of our blog series on converting legacy ETL to modern ETL! Before I begin the second part of this three-blog series, let’s recap the three key aspects under consideration for converting from legacy ETL to modern ETL.