MAD Data Podcast

Taylor Murphy, Head of Product and Data at Meltano, introduces the history and vision behind the open source brainchild that spun out of GitLab. In a world where businesses rely on a high variety of external data sources, many of which may be obscure and unique to an industry, the ability to develop custom connectors is indispensable. The growth of the Meltano community signals a larger trend where data teams are choosing tech stacks that adapt to their business contexts rather than the other way around. While data observability has become embraced as a data quality must-have, the industry recognizes that growing complexity calls for a distinctly proactive approach to data observability.

Matt Turck, creator of the term “MAD landscape” and Partner at FirstMark Capital, joins Joe Reis, CEO of Ternary Data and Monday Morning Data Chat podcast host, and Databand.ai Co-Founder & CEO, Josh Benamram, to discuss the explosion of data tooling, the creation of data observability as a new category, and what’s next in innovation and data engineering. What old problems have we solved? What new problems are emerging? What’s ahead in the next 5 years?

Nick Schrock, Founder & CEO of Elementl, and Scott Breitenother, Founder of Brooklyn Data Co., discuss the evolution of data from Big Data to Big Complexity – what’s next now that the data industry has solved the problem of data storage? While the modern data stack has become embraced as every data team’s “must-have” to address ‘modern data problems,’ Nick and Scott muse on the struggles that continue to plague data teams and the next wave of potential in data infrastructure innovation. With one problem solved, a new era of possibility and complexity is now unleashed.

Our modern data reality is highly complex. Data teams are recognizing that previous data processes made for a bygone era are tough to scale. Airbyte Co-Founder & CEO, Michel Tricot, and Databand.ai Co-Founder & CEO, Josh Benamram, offer insights and advice on what to do when the sheer number and variety of external data sources that a business counts on multiply at a dizzying rate – and show no sign of stopping. Our guests speak to the necessity of paying special attention to the left of the warehouse to ensure that all your data gets in and all your data is correct. In order to achieve data quality at scale and future-proof your data operations, data observability and data integration must work together hand-in-hand.

Josh Laurito, Director of Data at Squarespace and editor of NYC Data, shares his observations and insights on how data engineering has matured from a previously nebulous identity to a clearer purpose today. Josh, Harper, and Honor take a quick stroll down the data memory lane to take stock of all the changes and learnings that are shaping data engineering into a distinct discipline. The explosion in data tooling shows no sign of stopping – what does this mean for data engineers, data scientists, and data analysts who are growing in their understanding of what they love to do and the part they want to play?

Databand.ai Data Solution Architect, Michael Harper, joins as MAD Data co-host to share insights on how the treatment of data is rapidly evolving from a principle of least privileged to a principle of data access and data observability. Harper and Honor discuss what needs to occur in order for data to become available without security risk and the kind of education needed at all levels to advance data literacy. As data becomes an indispensable asset that directly contributes to the bottom line, one has to ask, Does it make sense to only empower a select few with essential business intelligence to make smart decisions?

Databricks Sr. Staff Developer Advocate, Denny Lee, Citadel Head of Business Engineering, Vinoo Ganesh, and Databand.ai Co-Founder & CEO, Josh Benamram, discuss the complexities and business necessity of setting clear data service-level agreements (SLAs). They share their experiences around the importance of contractual expectations and why data delivery success criteria are prone to disguise failures as success in spite of our best intentions. Denny, Vinoo, and Josh challenge businesses of all industries to see themselves as data companies by driving home a costly reality – what do businesses have to lose when their data is wrong? A lot more than they’d like to believe.

Databand.ai Director of Product, Shani Keynan, provides a fresh perspective on how to define data quality and how to control data quality when your data is in motion. Data observability is well understood to be a means to quality data. However, what’s often overlooked is the sheer distance that data must travel from the moment it’s collected all the way to data consumers. This means that data observability must be performed truly from end-to-end by starting right from the beginning (at the data ingestion layer) in order for data observability to be effective at all. Shani offers examples that illustrate how to make data quality an achievable goal and how to apply real-world logic and context create business impact.