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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.

There’s recently been an important debate taking place on LinkedIn around the topic of relational databases, joins and how that impacts a user’s experience with analytics.

Self-driving vehicles, weather forecasting drones, fulfilment robots and robotic surgery are already transforming the lives of millions of people. It is deep learning computer vision (DL CV) — visual sensors coupled with the ability to make instantaneous, human-like sense out of streaming video — that make these applications possible. One might think that acute focus on DL CV applications would be sufficient to yield the necessary breakthroughs and successful industry applications.

Almost three years ago today, to the day, Talend opened its fourth global research and development center, and its second one in France, in Nantes. It was clear to Talend from the very beginning of this new innovation center that it would not be a simple satellite of existing centers, but a key element in our strategy and overall R&D efforts.

As analytics software vendors, we need to do something profoundly different to improve how business users access, consume, and interact with data.