Data Quality

Keeping A Bigeye On The Data Quality Market - Episode 160

One of the oldest aphorisms about data is “garbage in, garbage out”, which is why the current boom in data quality solutions is no surprise. With the growth in projects, platforms, and services that aim to help you establish and maintain control of the health and reliability of your data pipelines it can be overwhelming to stay up to date with how they all compare. In this episode Egor Gryaznov, CTO of Bigeye, joins the show to explore the landscape of data quality companies, the general strategies that they are using, and what problems they solve. He also shares how his own product is designed and the challenges that are involved in building a system to help data engineers manage the complexity of a data platform. If you are wondering how to get better control of your own pipelines and the traps to avoid then this episode is definitely worth a listen.

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Better Data Quality Through Observability With Monte Carlo - Episode 155

In order for analytics and machine learning projects to be useful, they require a high degree of data quality. To ensure that your pipelines are healthy you need a way to make them observable. In this episode Barr Moses and Lior Gavish, co-founders of Monte Carlo, share the leading causes of what they refer to as data downtime and how it manifests. They also discuss methods for gaining visibility into the flow of data through your infrastructure, how to diagnose and prevent potential problems, and what they are building at Monte Carlo to help you maintain your data’s uptime.

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Data Management Trends From An Investor Perspective - Episode 136

The landscape of data management and processing is rapidly changing and evolving. There are certain foundational elements that have remained steady, but as the industry matures new trends emerge and gain prominence. In this episode Astasia Myers of Redpoint Ventures shares her perspective as an investor on which categories she is paying particular attention to for the near to medium term. She discusses the work being done to address challenges in the areas of data quality, observability, discovery, and streaming. This is a useful conversation to gain a macro perspective on where businesses are looking to improve their capabilities to work with data.

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