The DataAware Podcast

DataAware by Ascend is a community-focused podcast to discuss all things data automation.

With a variety of guests from all facets of data automation from data engineering and associated teams, each DataAware episode will look in-depth at the role of data automation and data teams, trends, best (and worst) practices, real world use cases, and more.

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Recent Episodes

Join Sean and Paul as they unpack the trends from Ascend’s annual DataAware Pulse Survey. Learn why executives and individual contributors disagree on strategy so often… and why many in the data team want to drive automation but struggle to achieve it. All that and a full recap of the 2023 Big Data London event in this episode!

Sean and Paul talk the three eras of data engineering teams move through as they get more mature with data processing. We unpack the kinds of metadata required at each stage, and how realistic it is to build a system that processes data in incremental packets instead of full reductions.

Sean and Paul talk about the impact that Generative AI will have on data engineering. They explore similarities between LLMs and data pipeline automation models, and review common components of an automation stack.

Sean and Paul talk about whether spreadsheets will become self-aware now that Python functions are available natively in Excel, and what the definition of Data Pipeline Automation is given that so many people say they want it.

Sean and Paul unpack the latest research on the benefits of automation and why platform consolidation happens so often during market downturns. They explore how data engineers can improve their productivity with data by 700% and save at least $156k on their data stack costs at the same time. Tune in for more!

The one where Paul and Sean talk about why Sean founded Ascend, how automation has disrupted so many parts of the developer stack in the past, and why it’s coming for data pipelines next!

In this episode, Sean and Leslie talk all things data lineage with Ascend solution engineer Jon Saltzman—from its importance at every step of the data journey to how data organizations go about ensuring their data is “certified fresh and organic” or, rather, easily traceable to where it’s been and who has touched it.

In this episode, we chat with Ascend Field Data Engineer Shayaan Saiyed about one thing data engineers can’t function without—data transformations, including best practices for iterating and troubleshooting and how data teams can be goal-oriented when starting to think about their ETL pipeline.

In this episode, we go back to the foundations of data engineering and data pipelines with Sheel Choksi to take a deep dive into data ingest—from where to start to how to not back yourself into a data pipeline corner and what different requirements different sources may need, and more.

In this episode, we sit down to discuss the concept of accidental ransomware—or when a team is slowed down by the burden of managing and maintaining outdated, commoditized, or otherwise decreasing in value software architectures or code. Learn how to spot the signs of accidental ransomware and negotiate the release in this week’s episode.

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In this episode, we do things a bit differently and chat with one of Ascend’s lead engineers, Nandan Tammineedi, about his work helping develop the Ascend for Snowflake platform, including what surprised him during development, what he’s enjoyed seeing since it has been in production and more.

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In this episode, Sean and Leslie talk deeper about a foundational aspect of data engineering workloads—automation. They look at the confusion that frequently surrounds data automation and how some data teams can avoid the major pitfall of going too far too fast, so they can realize all the benefits automation brings.

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In this episode, Sean and Leslie chat about one of the foundational principles of data engineering—data orchestration. What is it, who needs it (spoiler alert: everyone), and just how data engineering organizations, and data teams overall, need data orchestration to evolve over the next few years.

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In this episode, Sheel and Leslie had the chance to sit down with Shawn Azman, Head of Operations at Vidora, to chat about how organizations are moving forward with building out machine learning pipelines, overcoming common pitfalls, and overall, successfully operationalizing their machine learning practices.

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In this episode, we sat down to chat through the surprising (and not so surprising) results from our 2021 DataAware Pulse Survey, which takes a look at how the data engineering landscape is evolving and how data teams are changing with it to meet growing organizational needs.

In this episode, Leslie chats with Ascend’s own Joe Stevens about some of the recent infrastructure changes to the Ascend Platform, how this helps streamline users’ data infrastructure, and more.

In this episode, Sean and Leslie chatted with James Campbell, CTO of Superconductive—the team behind Great Expectations, about some of the intricacies of data validation as well as what it’s like to build an engaged community in the data realm.

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In this episode, Michael and Leslie dove in with Cyrus Facciano & Jared Langguth of X is Y, a New Zealand-based data consultancy, about the challenges that data teams—all the way up to the CDO—face in getting alignment on data, data strategy, and analytics outcomes.

In this episode, Sean and Leslie chatted with Miguel Alvarado, CTO at Lumiata, a company on the cutting edge of enabling ML & AI for healthcare organizations, about how data science is moving the needle in the healthcare industry, as well as what organizations should be looking for when it comes to AI & ML centric data teams.

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In this episode, we learn from Harry’s data analyst William Knighting and’s Sheel Choksi how the Harry’s data science team recently undertook an effort to expedite ingesting, transforming, and delivering new retail data feeds into a robust shared data model that connects all brand information across every retail delivery model.

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In this episode, we chat with Sheel Choksi about his path to data engineering, and what he’s seen as the biggest regrets data engineers have as they’re building out their systems—as well as how to avoid those regrets.

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In this episode, we chat with Jesse Anderson, managing director of the Big Data Institute and author of the new book Data Teams (available at, about the growth and change of data teams. We chat about best (and worst) practices for how data engineers, analysts, and scientists can work together now and in the future.

In this episode, we sat down to chat about’s new Flex Code data connectors and how they will help organizations speed—and simplify—data ingest for Spark-based data systems.

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In this inaugural episode, we chatted with’s founder and CEO, Sean Knapp, about the trends we’ve been seeing in the data engineering realm, as well as the shift the data engineering function has taken over the last several years.

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