Speaker: Jason Reid (Co-founder & Head of Product at Tabular)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. Unbundling a data warehouse means splitting it into constituent and modular components that interact via open standard interfaces. In this talk, Jason Reid discusses the pros and cons of both data warehouse bundling and unbundling in terms of performance, governance, and flexibility, and he examines how the trend of data warehouse unbundling will impact the data engineering landscape in the next 5 years.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Speaker: Jide Ogunjobi (Founder & CTO at Context Data)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. As organizations accumulate ever-larger stores of data across disparate systems, efficiently querying and gaining insights from enterprise data remain ongoing challenges. To address this, we propose developing an intelligent agent that can automatically discover, map, and query all data within an enterprise. This “Enterprise Data Model/Architect Agent” employs generative AI techniques for autonomous enterprise data modeling and architecture.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Speaker: Iaroslav Zeigerman (Co-Founder and Chief Architect at Tobiko Data)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. The development and evolution of data pipelines are hindered by outdated tooling compared to software development. Creating new development environments is cumbersome: Populating them with data is compute-intensive, and the deployment process is error-prone, leading to higher costs, slower iteration, and unreliable data. SQLMesh, an open-source project born from our collective experience at companies like Airbnb, Apple, Google, and Netflix, is designed to handle the complexities of evolving data pipelines at an internet scale. In this talk, Iaroslav Zeigerman discusses challenges faced by data practitioners today and how core SQLMesh concepts solve them.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Speakers: Stephanie Vezich Tamayo (Senior Machine Learning Engineer at Netflix) Binbing Hou (Senior Software Engineer at Netflix)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. At Netflix, hundreds of thousands of workflows and millions of jobs are running every day on our big data platform, but diagnosing and remediating job failures can impose considerable operational burdens. To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.” However, as the system has increased in scale and complexity, Pensive has been facing challenges due to its limited support for operational automation, especially for handling memory configuration errors and unclassified errors. To address these challenges, we have developed a new feature called “Auto Remediation,” which integrates the rules-based classifier with an ML service.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Speaker: Tulika Bhatt (Senior Data Engineer at Netflix)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. Netflix generates approximately 18 billion impressions daily. These impressions significantly influence a viewer’s browsing experience, as they are essential for powering video ranker algorithms and computing adaptive pages, With the evolution of user interfaces to be more responsive to in-session interactions, coupled with the growing demand for real-time adaptive recommendations, it has become highly imperative that these impressions are provided on a near real-time basis. This talk will delve into the creative solutions Netflix deploys to manage this high-volume, real-time data requirement while balancing scalability and cost.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Speaker: Jessica Larson (Data Engineer & Author of “Snowflake Access Control”)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. The requirements for creating a new data warehouse in the post-GDPR world are significantly different from those of the pre-GDPR world, such as the need to prioritize sensitive data protection and regulatory compliance over performance and cost. In this talk, Jessica Larson shares her takeaways from building a new data platform post-GDPR.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Max Schmeiser (Vice President of Studio and Content Data Science & Engineering) extends a warm welcome to all attendees, marking the beginning of our inaugural Data Engineering Open Forum.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

Speaker: Clark Wright (Staff Analytics Engineer at Airbnb)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. Recently, Airbnb published a post to their Tech Blog called Data Quality Score: The next chapter of data quality at Airbnb. In this talk, Clark Wright shares the narrative of how data practitioners at Airbnb recognized the need for higher-quality data and then proposed, conceptualized, and launched Airbnb’s first Data Quality Score.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.