MAD Data Podcast

Every month, we talk to experts like you who use machine learning, AI, and data to transform their businesses. Join us as we highlight the stories that shape the fields of data engineering, science, and analytics both for today and the future. Hosted by Databand’s Co-Founder and CEO, Josh Benamram, and Chief Marketing Officer, Ryan Yackel.

MAD Data Podcast

Prefer to watch instead? Check out our podcast video page.

Podcasts

How To Reduce Data System Complexities

Identifying the key factors of data system complexities and how to reduce them is critical for any company. Joseph Machado, Senior Data Engineer at LinkedIn, shares his insights on ways to keep data systems from becoming unwieldy. In addition, Joseph offers tips for data teams to manage their data warehouses and keep data pipelines running reliably.

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Podcasts

How Incident Management Supplements Data Governance

Matthew Blasa, a Data Scientist at The Home Depot, stopped by the MAD Data Studio to talk about how incident management as a part of observability and governance can affect various data areas. Learn how Matthew and his team tackle data observability, as well as different ways of approaching data governance on a small scale versus an enterprise scale.

 

Check out Matt’s Medium @ blaza-matt and his YouTube channel @ DataLife360

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Podcasts

Lessons Learned Building World-Class Data Engineering Teams

Sid Anand, Chief Architect & Head of Engineering at Datazoom, dropped by to give his lessons learned in building world-class data engineering teams at Ebay, Netflix, LinkedIn, and PayPal. For anyone who’s leading a team now or in the future, you don’t want to miss Sid’s insights in working at some of the world’s biggest brands.

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Podcasts

The Case for Dataset Centric Visualization

We love Maxime so much that we had him back for a second podcast! In this episode, Max makes the case for dataset centric visualization. He walks through how different BI tools offer different approaches to building dashboards. Then he discusses how dataset centric modeling is a powerful approach that combines the best of the query-centric and semantic-centric visualization approaches.

 

Check out Maxime’s blog post here – The Case for Dataset-Centric Visualization

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Podcasts

Unveiling The IBM Acquisition Of Databand

In our first two-part guest special we’re happy to introduce, Maxime Beauchemin, CEO and Founder of Preset. Eager to learn more about the recent IBM acquisition of Databand, Maxime asks us to describe it in more detail and explains what it means for the future of the data observability space.

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Podcasts

Two Philadelphians Talk Data Startups And Building An Analytics Community

Tristan Handy is the Founder & CEO of dbt Labs, creators, and maintainers of dbt. dbt Labs is building the modern analytics workflow used by tens of thousands of data analysts. Tristan stopped by to talk to our CEO, and fellow Philadelphian, Josh Benamram about how he founded and built dbt Labs, and how they are building an analytics community.

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Podcasts

How Pinterest Is Migrating To The Modern Data Cloud With Confidence

Jessica Larson is a Data Engineer at Pinterest and the author of Snowflake Access Control: Mastering the Features for Data Privacy and Regulatory Compliance. Jessica stopped by to talk about how Pinterest is migrating to the modern data cloud with Snowflake and the lessons learned in her new book.

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Podcasts

How To Avoid The Vortex Of Data Debt

Chad Sanderson Head of Product and Data Platform at Convoy, a digital freight network that’s solving problems in the $800B trucking industry. Chad has a lot on his plate, including being responsible for Convoy’s Data Engineering team, Data Warehouse, Data Pipeline tooling, Experimentation Platform, Machine Learning Platform, Analytics Platform, and Streaming. Chad swung by to talk about how his team is building one of the most advanced experimentations and machine learning platforms in the world from the ground up.

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Podcasts

How To Treat Data As A Product

Colleen Tartow is the Director of Engineering for Starburst Data, a product suite that gives users the ability to self-manage their data infrastructure. We had a great time in the MAD Data studio as she shared insights on how data-driven companies should treat their data as a product and dove into the category of data mesh. Colleen also holds a Ph.D. in astrophysics which makes her truly out of this world!

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Podcasts

Lights On Your Dark Data Quality Issues

George Firican is an award-winning data governance leader, founder of LightsOnData, and podcast host of the Lights On Data Show. George swung by the MAD Data studio to talk about what actually makes a company data-driven and why companies need to be more aware of their data quality issues.

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Podcasts

The Case For Data Catalogs: Are Your Teams On The Same Page?

As companies scale out their data operations and data demands continue to mount, communications and knowledge alignment form every team’s operational backbone. Castor CTO, Amaury Dumoulin, explains the foundational importance of data catalogs as teams expand in size and knowledge.

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Podcasts

Where Should Data Quality Sit? It Depends.

Data quality is universally important to data teams but where data quality checks should sit along the data process depends on your business use case. Netflix Sr. Software Engineer (Data Pipelines), Vivek Kaushal, shares his experience on the differences in data quality criteria from his time at Apple, Amazon, and now at Netflix.

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Podcasts

Proactive Data Quality for Data-Intensive Organizations

How do you ensure data quality when your business relies on data from a high variety of external data sources? Johannes Leppä, Sr. Data Engineer at Komodo Health, shares his insights on how a data-intensive operation can design its data infrastructure to prevent common errors and high-impact issues. Johannes offers his tips for data teams to get ahead of data errors and proactively manage data quality.

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Podcasts

Proactive Data Quality: Why Prevention Is The Best Cure

Following up on our last episode on data quality, this episode takes aim at a common culture of reactivity in data teams. Gary Cheung, Staff Analytics Engineer at Eventribite, highlights the importance of taking a proactive approach to data quality and why prevention is the cure that every data team needs.

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Podcasts

Data Quality: What's Your Plan?

Data quality is the data industry’s holy grail – desired by all but mysteriously elusive. Sam Bail, who speaks frequently on the topic of data quality, joins Sarah Krasnik, Data Engineer at Perpay, to discuss the building blocks, organizational mindset, and strategic planning that are needed to take data quality from theory to practice.

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Podcasts

Built For Complexity: Data Observability Meets Data Integration

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.

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Podcasts

What's Next in Data Observability & Data Tooling

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?

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Podcasts

Hello Big Complexity: Is Your Modern Data Stack Ready?

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.

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Podcasts

Modern Data Love: Why Data Observability & Data Integration Belong Together

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.

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Podcasts

Data Engineering Comes Of Age: Choosing A Path That Resonates With What You Love

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?

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Podcasts

Data Is Everyone's Business: The Case For Data Access & Data Observability

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?

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Podcasts

Defining Data Quality: Data SLA Nightmares & Lessons Learned

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.

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Podcasts

Why Data Quality Begins At The Source

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.

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