2022 Data Automation Summit Library

2022 Data Automation Summit Library

Watch all sessions on demand and walk through how some of today’s most innovative companies are using automation to create highly sophisticated data platforms and products.


Sean Knapp, Founder and CEO of, welcomes you to the 2022 Data Automation Summit with a discussion of the rise of automation’s importance in today’s modern data stack. Then, Sean sits down with Ascend’s Tom Weeks for a fireside chat about what attendees can look forward to seeing at this year’s event.​

Sean Knapp sits down with former U.S. Chief Data Scientist, DJ Patil, to discuss how the role of the data scientist has evolved and solidified over the last 10-15 years and what the next 15+ years may bring with the rise of data automation technologies.

Nandan Tammineedi, Tech Lead at, explains the evolution in the architecture behind the Ascend Data automation Cloud and how data planes give data professionals flexibility when choosing compute and storage engines.

Data Orchestration

Tom Kozlowski, VP of Decision Science and Analytics at HNI Corporation, discusses Speed to Data Insights: the HNI Approach to get a step closer to enabling analysts easy access to data.

How does automation accelerate data workloads for user consumption? Dai Tran, Sr. Manager, Analytics & Strategy at Skydio, dives into the data warehouse rebuild process and how Ascend enabled his team to centralize data pipelines and reporting.

How did Haejun Lee, Data Engineer at Komodo Health, and Upendra Chitnis, Senior Data Engineer at Komodo Health, automate the Komodo Health Data Platform? Watch their session to discover all the details.

Automation and Society

Iris Hauser, Data Science Center & Cloud Manager at Maytronics, and Giorgio Adami, Analytics Service Line Manager at Moviri, dive into cloud architecture design and configuration to provide maximum data availability to the final users.

Mindstrong collects phone typing data to extract features around typing dynamics.This talk will cover how they go into those logs describing the end to end pipeline—from collection to how they validate from science literature.

Jared Naynaert, Director of Analytics at Crane Worldwide Logistics, dives into how his team approaches developer experience building on top of the modern data stack. In other words, how they approach the data platform as a product.


The X is Y team discuss how they use automation to enable value and meet expectations on the DataOps track at the Data Automation Summit.

How do you embark upon big data initiatives? How do you get started and make sure you stay on track? Discover how Vanda Collins approached the Rail Innovation Development Solution initiative at Hitachi Rail Honolulu JV.

Analytics and Machine Learning

There is increased interest around real-time machine learning which enables a business to leverage real-time user signals, in the millisecond time-range, and use those signals to optimize consumer experiences. Hear Shawn Azman, Head of Customer Operations at Vidora, how underlying any production real-time machine learning deployment is data automation.

Have you found it hard to join batch and streaming data together and provide a single access point to your data consumer? If you have, watch Jove Kuang session, Automating Batch + Streaming Analytics with Ascend and Imply.

A fireside chat discussing how Dr. Ratneesh Suri, Head of Data Analytics at the Ports of Auckland, began her career in data team leadership and what important lessons she’s learned as she’s worked in and led some of the world’s largest data analytics organizations.

Data Observability

Most companies have invested in world-class data infrastructure. However, the data powering those systems is often wrong. Barr Moses, co-founder and CEO of Monte Carlo, discusses how to eliminate data downtime and how to start trusting your data. 

Getting started with a data quality project can present a major challenge. In this talk, James Campbell will discuss a modular, composable framework for building Expectations about data using components built around asking good questions about data.

Automation at Amazon Web Services

Interested in the Ascend Data Automation Cloud powered by AWS Glue? Watch Zach Mitchell, Sr. Big Data Architect at Amazon Web Services (AWS), and Nandan Tammineedi, Tech Lead at, session at the Data Automation Summit to learn all the details.

In this session, learn how Amazon Redshift automatically provisions data warehouse capacity and intelligently scales the underlying resources to deliver consistently high performance and simplified operations for even the most demanding and volatile workloads.

Discover how Ascend presents a new class of software that solves many of the hundreds of edge cases unique to data pipelines, empowering data engineers to implement and operationalize data pipelines across many of the AWS services far faster and more reliably than ever before.

Data Architectures

Data professionals frequently struggle to secure funding for projects, fail to deliver sufficient value when funded, and see architectural decisions determined by a team with a less technical understanding of data. In this session, Jacob H. Levy, Principal Data Architect at Entisys360, will explore how helps data teams deliver compounding, measurable value to the enterprise.

Lean engineering teams and tight budgets can keep many data engineering projects sidelined at tech startups. Paul DeSalvo, Data Engineering Manager at Brazen, talks about the evolution of data challenges at a fast-growing startup and how they solved them without breaking the bank. 

In this presentation, Pietro Guardati, Data Engineer at Kpler, shows an example of how companies can treat proprietary data to create business value—from the storage of raw records to the deployment of a new containerized application. Overall, the development of an Automated MLOps pipeline is described.

The People and Processes of Automation

Ben Tallman, CTO at M Science, chats with Tom Weeks, Chief Business Officer at, about data scale and what are the opportunities and challenges they face when tagging, analyzing, and delivering 60B rows of data.

Tony Dahlager, Managing Director – Data Management at Analytics8, discusses why it is crucial to care about the impact of your data team’s work, the common challenges that often prevent solutions from bringing maximum value, the secret sauce to developing valuable solutions, and real-life examples of building technology solutions that bring business impact.

Dive into three eras of data engineering at Fivestars a.k.a the mess CTO, Matt Doka, inherited when he took over data engineering and the best practices he distilled.

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