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Abstract:AI promises transformation, but most enterprises are still held back by a blind spot: unstructured data. The documents, conversations, and content that hold the richest business insights remain locked away—unusable, ungoverned, and invisible to AI. This untapped treasure delays projects, derails accuracy, and creates governance risks.

In this exclusive launch session, Teresa Rojas, Senior Director of Product Management at Collibra, and Reece Griffiths, co-founder of Deasy Labs (now part of Collibra), will unveil Collibra Unstructured AI. They’ll show how breakthrough technology, combined with Collibra’s unified governance platform, enables organizations to finally move beyond the black box and unlock the hidden wealth in their data to power trustworthy, scalable AI.

Expect a hands-on journey showing how modern data lake tools and governance platforms connect the dots - making your data discoverable, governed, and productized for real-world use.

Stream processing systems have traditionally relied on local storage engines such as RocksDB to achieve low latency. While effective in single-node setups, this model doesn't scale well in the cloud, where elasticity and separation of compute and storage are essential. In this talk, we'll explore how RisingWave rethinks the architecture by building directly on top of S3 while still delivering sub-100 ms latency. At the core is Hummock, a log-structured state engine designed for object storage. Hummock organizes state into a three-tier hierarchy: in-memory cache for the hottest keys, disk cache managed by Foyer for warm data, and S3 as the persistent cold tier. This approach ensures queries never directly hit S3, avoiding its variable performance. We'll also examine how remote compaction offloads heavy maintenance tasks from query nodes, eliminating interference between user queries and background operations. Combined with fine-grained caching policies and eviction strategies, this architecture enables both consistent query performance and cloud-native elasticity. Attendees will walk away with a deeper understanding of how to design streaming systems that balance durability, scalability, and low latency in an S3-based environment.

Data teams are faced with increased demand for trusted data assets, but struggle to quickly and safely grant access to the most validated data. In this session, Schroders will share how they've enhanced discoverability and governance of data across the enterprise using Collibra's unified governance for data and AI to enable data consumers to easily find and access the trusted data they need. Learn how building a curated marketplace of data products has helped foster greater collaboration and increased the safe usage of data for greater advantage.

Summary In this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world; like in their episode “The Secret Sauce Behind McDonald’s Data Strategy”, which digs into how AI-driven tools can be used to support crew efficiency and customer interactions. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of businessInterview IntroductionHow did you get involved in the area of data management?Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled?How does that reconciliation relate to the practice of "master data management"What are the scaling challenges with the current set of practices for reconciling data?ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years?What (if any) transformative capabilities do LLMs introduce?What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning?What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process?What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams?What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution?What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management?When is ML/AI the wrong choice for data cleaning/reconciliation?What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links TamrMaster Data ManagementCERNLHCMichael StonebrakerConway's LawExpert SystemsInformation RetrievalActive LearningThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary The core task of data engineering is managing the flows of data through an organization. In order to ensure those flows are executing on schedule and without error is the role of the data orchestrator. Which orchestration engine you choose impacts the ways that you architect the rest of your data platform. In this episode Hugo Lu shares his thoughts as the founder of an orchestration company on how to think about data orchestration and data platform design as we navigate the current era of data engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementIt’s 2024, why are we still doing data migrations by hand? Teams spend months—sometimes years—manually converting queries and validating data, burning resources and crushing morale. Datafold's AI-powered Migration Agent brings migrations into the modern era. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today to learn how Datafold can automate your migration and ensure source to target parity. As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us don't miss Data Citizens® Dialogues, the forward-thinking podcast brought to you by Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. In every episode of Data Citizens® Dialogues, industry leaders unpack data’s impact on the world, from big picture questions like AI governance and data sharing to more nuanced questions like, how do we balance offense and defense in data management? In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. The Data Citizens Dialogues podcast is bringing the data conversation to you, so start listening now! Follow Data Citizens Dialogues on Apple, Spotify, YouTube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Hugo Lu about the data platform and orchestration ecosystem and how to navigate the available optionsInterview IntroductionHow did you get involved in building data platforms?Can you describe what an orchestrator is in the context of data platforms?There are many other contexts in which orchestration is necessary. What are some examples of how orchestrators have adapted (or failed to adapt) to the times?What are the core features that are necessary for an orchestrator to have when dealing with data-oriented workflows?Beyond the bare necessities, what are some of the other features and design considerations that go into building a first-class dat platform or orchestration system?There have been several generations of orchestration engines over the past several years. How would you characterize the different coarse groupings of orchestration engines across those generational boundaries?How do the characteristics of a data orchestrator influence the overarching architecture of an organization's data platform/data operations?What about the reverse?How have the cycles of ML and AI workflow requirements impacted the design requirements for data orchestrators?What are the most interesting, innovative, or unexpected ways that you have seen data orchestrators used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data orchestration?When is an orchestrator the wrong choice?What are your predictions and/or hopes for the future of data orchestration?Contact Info MediumLinkedInParting Question From your perspective, what is the biggest thing data teams are missing in the technology today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links OrchestraPrevious Episode: Overview Of The State Of Data OrchestrationCronArgoCDDAGKubernetesData MeshAirflowSSIS == SQL Server Integration ServicesPentahoKettleDataVoloNiFiPodcast EpisodeDagstergRPCCoalescePodcast EpisodedbtDataHubPalantirThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode of the Data Engineering Podcast, Anna Geller talks about the integration of code and UI-driven interfaces for data orchestration. Anna defines data orchestration as automating the coordination of workflow nodes that interact with data across various business functions, discussing how it goes beyond ETL and analytics to enable real-time data processing across different internal systems. She explores the challenges of using existing scheduling tools for data-specific workflows, highlighting limitations and anti-patterns, and discusses Kestra's solution, a low-code orchestration platform that combines code-driven flexibility with UI-driven simplicity. Anna delves into Kestra's architectural design, API-first approach, and pluggable infrastructure, and shares insights on balancing UI and code-driven workflows, the challenges of open-core business models, and innovative user applications of Kestra's platform.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.As a listener of the Data Engineering Podcast you clearly care about data and how it affects your organization and the world. For even more perspective on the ways that data impacts everything around us you should listen to Data Citizens® Dialogues, the forward-thinking podcast from the folks at Collibra. You'll get further insights from industry leaders, innovators, and executives in the world's largest companies on the topics that are top of mind for everyone. They address questions around AI governance, data sharing, and working at global scale. In particular I appreciate the ability to hear about the challenges that enterprise scale businesses are tackling in this fast-moving field. While data is shaping our world, Data Citizens Dialogues is shaping the conversation. Subscribe to Data Citizens Dialogues on Apple, Spotify, Youtube, or wherever you get your podcasts.Your host is Tobias Macey and today I'm interviewing Anna Geller about incorporating both code and UI driven interfaces for data orchestrationInterview IntroductionHow did you get involved in the area of data management?Can you start by sharing a definition of what constitutes "data orchestration"?There are many orchestration and scheduling systems that exist in other contexts (e.g. CI/CD systems, Kubernetes, etc.). Those are often adapted to data workflows because they already exist in the organizational context. What are the anti-patterns and limitations that approach introduces in data workflows?What are the problems that exist in the opposite direction of using data orchestrators for CI/CD, etc.?Data orchestrators have been around for decades, with many different generations and opinions about how and by whom they are used. What do you see as the main motivation for UI vs. code-driven workflows?What are the benefits of combining code-driven and UI-driven capabilities in a single orchestrator?What constraints does it necessitate to allow for interoperability between those modalities?Data Orchestrators need to integrate with many external systems. How does Kestra approach building integrations and ensure governance for all their underlying configurations?Managing workflows at scale across teams can be challenging in terms of providing structure and visibility of dependencies across workflows and teams. What features does Kestra offer so that all pipelines and teams stay organised?What are

A data lake is only as good as its governance. Understanding what data you have, performing classification, defining/applying security policies and auding how it's used is the data governance lifecycle. Unity Catalog with its rich ecosystem of supported tools simplifies all stages of the data governance lifecycle. Learn how metadata can be hydrated, into Collibra directly from Unity Catalog. Once the metadata is available in Collibra we will demonstrate classification, defining security policies on the data and pushing those policies into Databricks. All access and usage of data is automatically audited with real time lineage provided in the data explorer as well as system tables.

Talk by: Leon Eller and Antonio Castelo

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