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.

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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.

Until late last year, Rainforest ran most of our production applications on Heroku. Heroku was a terrific platform for Rainforest in many ways: it allowed us to scale and remain agile without hiring a large Ops team, and the overall developer experience is unparalleled. But in 2018 it became clear that we were beginning to outgrow Heroku.

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.