Summary In this episode of the Data Engineering Podcast Matt Topper, president of UberEther, talks about the complex challenge of identity, credentials, and access control in modern data platforms. With the shift to composable ecosystems, integration burdens have exploded, fracturing governance and auditability across warehouses, lakes, files, vector stores, and streaming systems. Matt shares practical solutions, including propagating user identity via JWTs, externalizing policy with engines like OPA/Rego and Cedar, and using database proxies for native row/column security. He also explores catalog-driven governance, lineage-based label propagation, and OpenTDF for binding policies to data objects. The conversation covers machine-to-machine access, short-lived credentials, workload identity, and constraining access by interface choke points, as well as lessons from Zanzibar-style policy models and the human side of enforcement. Matt emphasizes the need for trust composition - unifying provenance, policy, and identity context - to answer questions about data access, usage, and intent across the entire data path.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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.Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.Your host is Tobias Macey and today I'm interviewing Matt Topper about the challenges of managing identity and access controls in the context of data systemsInterview IntroductionHow did you get involved in the area of data management?The data ecosystem is a uniquely challenging space for creating and enforcing technical controls for identity and access control. What are the key considerations for designing a strategy for addressing those challenges?For data acess the off-the-shelf options are typically on either extreme of too coarse or too granular in their capabilities. What do you see as the major factors that contribute to that situation?Data governance policies are often used as the primary means of identifying what data can be accesssed by whom, but translating that into enforceable constraints is often left as a secondary exercise. How can we as an industry make that a more manageable and sustainable practice?How can the audit trails that are generated by data systems be used to inform the technical controls for identity and access?How can the foundational technologies of our data platforms be improved to make identity and authz a more composable primitive?How does the introduction of streaming/real-time data ingest and delivery complicate the challenges of security controls?What are the most interesting, innovative, or unexpected ways that you have seen data teams address ICAM?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ICAM?What are the aspects of ICAM in data systems that you are paying close attention to?What are your predictions for the industry adoption or enforcement of those controls?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 UberEtherJWT == JSON Web TokenOPA == Open Policy AgentRegoPingIdentityOktaMicrosoft EntraSAML == Security Assertion Markup LanguageOAuthOIDC == OpenID ConnectIDP == Identity ProviderKubernetesIstioAmazon CEDAR policy languageAWS IAMPII == Personally Identifiable InformationCISO == Chief Information Security OfficerOpenTDFOpenFGAGoogle ZanzibarRisk Management FrameworkModel Context ProtocolGoogle Data ProjectTPM == Trusted Platform ModulePKI == Public Key InfrastructurePassskeysDuckLakePodcast EpisodeAccumuloJDBCOpenBaoHashicorp VaultLDAPThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Summary In this episode Kate Shaw, Senior Product Manager for Data and SLIM at SnapLogic, talks about the hidden and compounding costs of maintaining legacy systems—and practical strategies for modernization. She unpacks how “legacy” is less about age and more about when a system becomes a risk: blocking innovation, consuming excess IT time, and creating opportunity costs. Kate explores technical debt, vendor lock-in, lost context from employee turnover, and the slippery notion of “if it ain’t broke,” especially when data correctness and lineage are unclear. Shee digs into governance, observability, and data quality as foundations for trustworthy analytics and AI, and why exit strategies for system retirement should be planned from day one. The discussion covers composable architectures to avoid monoliths and big-bang migrations, how to bridge valuable systems into AI initiatives without lock-in, and why clear success criteria matter for AI projects. Kate shares lessons from the field on discovery, documentation gaps, parallel run strategies, and using integration as the connective tissue to unlock data for modern, cloud-native and AI-enabled use cases. She closes with guidance on planning migrations, defining measurable outcomes, ensuring lineage and compliance, and building for swap-ability so teams can evolve systems incrementally instead of living with a “bowl of spaghetti.”

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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.Your host is Tobias Macey and today I'm interviewing Kate Shaw about the true costs of maintaining legacy systemsInterview IntroductionHow did you get involved in the area of data management?What are your crtieria for when a given system or service transitions to being "legacy"?In order for any service to survive long enough to become "legacy" it must be serving its purpose and providing value. What are the common factors that prompt teams to deprecate or migrate systems?What are the sources of monetary cost related to maintaining legacy systems while they remain operational?Beyond monetary cost, economics also have a concept of "opportunity cost". What are some of the ways that manifests in data teams who are maintaining or migrating from legacy systems?How does that loss of productivity impact the broader organization?How does the process of migration contribute to issues around data accuracy, reliability, etc. as well as contributing to potential compromises of security and compliance?Once a system has been replaced, it needs to be retired. What are some of the costs associated with removing a system from service?What are the most interesting, innovative, or unexpected ways that you have seen teams address the costs of legacy systems and their retirement?What are the most interesting, unexpected, or challenging lessons that you have learned while working on legacy systems migration?When is deprecation/migration the wrong choice?How have evolutionary architecture patterns helped to mitigate the costs of system retirement?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 SnapLogicSLIM == SnapLogic Intelligent ModernizerOpportunity CostSunk Cost FallacyData GovernanceEvolutionary ArchitectureThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

The role of data analysts is evolving, not disappearing. With generative AI transforming the industry, many wonder if their analytical skills will soon become obsolete. But how is the relationship between human expertise and AI tools really changing? While AI excels at coding, debugging, and automating repetitive tasks, it struggles with understanding complex business problems and domain-specific challenges. What skills should today's data professionals focus on to remain relevant? How can you leverage AI as a partner rather than viewing it as a replacement? The balance between technical expertise and business acumen has never been more critical in navigating this changing landscape. Mo Chen is a Data & Analytics Manager with over seven years of experience in financial and banking data. Currently at NatWest Group, Mo leads initiatives that enhance data management, automate reporting, and improve decision-making across the organization. After earning an MSc in Finance & Economics from the University of St Andrews, Mo launched a career in risk and credit portfolio management before transitioning into analytics. Blending economics, finance, and data engineering, Mo is skilled at turning large-scale financial data into actionable insight that supports efficiency and strategic planning. Beyond corporate life, Mo has become a passionate educator and community-builder. On YouTube, Mo hosts a fast-growing channel (185K+ subscribers, with millions of views) where he breaks down complex analytics concepts into bite-sized, actionable lessons. In the episode, Richie and Mo explore the evolving role of data analysts, the impact of AI on coding and debugging, the importance of domain knowledge for career switchers, effective communication strategies in data analysis, and much more. Links Mentioned in the Show: Mo’s Website - Build a Data Portfolio WebsiteMo’s YouTube ChannelConnect with MoGet Certified as a Data AnalystRelated Episode: Career Skills for Data Professionals with Wes Kao, Co-Founder of MavenRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Summary In this episode of the Data Engineering Podcast, host Tobias Macey welcomes back Nick Schrock, CTO and founder of Dagster Labs, to discuss Compass - a Slack-native, agentic analytics system designed to keep data teams connected with business stakeholders. Nick shares his journey from initial skepticism to embracing agentic AI as model and application advancements made it practical for governed workflows, and explores how Compass redefines the relationship between data teams and stakeholders by shifting analysts into steward roles, capturing and governing context, and integrating with Slack where collaboration already happens. The conversation covers organizational observability through Compass's conversational system of record, cost control strategies, and the implications of agentic collaboration on Conway's Law, as well as what's next for Compass and Nick's optimistic views on AI-accelerated software engineering.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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. Your host is Tobias Macey and today I'm interviewing Nick Schrock about building an AI analyst that keeps data teams in the loopInterview IntroductionHow did you get involved in the area of data management?Can you describe what Compass is and the story behind it?context repository structurehow to keep it relevant/avoid sprawl/duplicationproviding guardrailshow does a tool like Compass help provide feedback/insights back to the data teams?preparing the data warehouse for effective introspection by the AILLM selectioncost managementcaching/materializing ad-hoc queriesWhy Slack and enterprise chat are important to b2b softwareHow AI is changing stakeholder relationshipsHow not to overpromise AI capabilities How does Compass relate to BI?How does Compass relate to Dagster and Data Infrastructure?What are the most interesting, innovative, or unexpected ways that you have seen Compass used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Compass?When is Compass the wrong choice?What do you have planned for the future of Compass?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 DagsterDagster LabsDagster PlusDagster CompassChris Bergh DataOps EpisodeRise of Medium Code blog postContext EngineeringData StewardInformation ArchitectureConway's LawTemporal durable execution frameworkThe 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 Vijay Subramanian, founder and CEO of Trace, talks about metric trees - a new approach to data modeling that directly captures a company's business model. Vijay shares insights from his decade-long experience building data practices at Rent the Runway and explains how the modern data stack has led to a proliferation of dashboards without a coherent way for business consumers to reason about cause, effect, and action. He explores how metric trees differ from and interoperate with other data modeling approaches, serve as a backend for analytical workflows, and provide concrete examples like modeling Uber's revenue drivers and customer journeys. Vijay also discusses the potential of AI agents operating on metric trees to execute workflows, organizational patterns for defining inputs and outputs with business teams, and a vision for analytics that becomes invisible infrastructure embedded in everyday decisions.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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.Your host is Tobias Macey and today I'm interviewing Vijay Subramanian about metric trees and how they empower more effective and adaptive analyticsInterview IntroductionHow did you get involved in the area of data management?Can you describe what metric trees are and their purpose?How do metric trees relate to metric/semantic layers?What are the shortcomings of existing data modeling frameworks that prevent effective use of those assets?How do metric trees build on top of existing investments in dimensional data models?What are some strategies for engaging with the business to identify metrics and their relationships?What are your recommendations for storage, representation, and retrieval of metric trees?How do metric trees fit into the overall lifecycle of organizational data workflows?When creating any new data asset it introduces overhead of maintenance, monitoring, and evolution. How do metric trees fit into existing testing and validation frameworks that teams rely on for dimensional modeling?What are some of the key differences in useful evaluation/testing that teams need to develop for metric trees?How do metric trees assist in context engineering for AI-powered self-serve access to organizational data?What are the most interesting, innovative, or unexpected ways that you have seen metric trees used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on metric trees and operationalizing them at Trace?When is a metric tree the wrong abstraction?What do you have planned for the future of Trace and applications of metric trees?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 Metric TreeTraceModern Data StackHadoopVerticaLuigidbtRalph KimballBill InmonMetric LayerDimensional Data WarehouseMaster Data ManagementData GovernanceFinancial P&L (Profit and Loss)EBITDA ==Earnings before interest, taxes, depreciation and amortizationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

AI is no longer a distant concept; it's here, reshaping the way we live and work. From coding and customer service to creative content, AI is already taking on tasks once thought to be uniquely human. But what does that mean for the future of work, and more importantly, for the role of leaders? In this solo episode of Hub & Spoken, Jason Foster, CEO and Founder of Cynozure, explores the real implications of AI on jobs, leadership, and human value. Drawing lessons from history, automation, shipping containers, even the rise of personal computing, Jason argues that every wave of technology has shifted humans "up a level of abstraction," moving us from doing to designing, to directing and innovating. He sets out four essential human traits to thrive in the age of AI: Think bigger – focus on outcomes, strategy, and imagination Lead differently – provide clarity, orchestrate teams, and build culture Connect deeper – lean into empathy, context, and trust Grow and adapt – stay curious, resilient, and open to change 🎧 Tune in to hear Jason's take on how we can design the future we want to be part of.

Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation.   

Nous transformons vos données en un capital maîtrisé et fiable, en plaçant le pilotage des usages au cœur de notre démarche. 

Notre expertise en gouvernance des données s’appuie sur la compréhension fine des usages métiers, afin d’identifier les enjeux de qualité et de garantir l’accès à une information réellement fiable et pertinente pour chaque utilisateur.

Nos équipes accompagnent votre Data Office pour piloter et encadrer les usages de la donnée, assurant ainsi conformité, fiabilité et adoption. 

Grâce à des contrôles rigoureux, des indicateurs adaptés et une méthodologie éprouvée chez nos clients, nous couvrons l’ensemble du cycle de vie de la donnée : Data Management, MDM, qualité et catalogues de données.

Optimisez la gestion de vos données et renforcez votre stratégie d’entreprise en gouvernant la data par les usages, avec notre expérience reconnue.

Pour cela venez voir un de nos accélérateurs Data lors du prochain Salon Data & IA de Paris !

Comment un grand groupe bancaire peut-il structurer sa gouvernance des données pour répondre aux exigences réglementaires tout en créant de la valeur pour les métiers ?

Michel Lunetta, Group Product Owner en Data Management chez BPCE, partagera le parcours du groupe — des premières initiatives menées chez Natixis à la généralisation d’un cadre de gouvernance unifié à l’échelle de l’entreprise.

Il reviendra sur les leviers activés pour renforcer la traçabilité, faciliter la documentation et assurer une mise en conformité efficace, grâce à une approche agile, modulaire et centrée sur les usages métiers.

Cette transformation progressive, soutenue par la plateforme Actian Data Intelligence (anciennement Zeenea), a permis de poser les bases d’un catalogue de données de confiance, au service de la conformité… et de la valorisation des actifs data.

Nous transformons vos données en un capital maîtrisé et fiable, en plaçant le pilotage des usages au cœur de notre démarche. 

Notre expertise en gouvernance des données s’appuie sur la compréhension fine des usages métiers, afin d’identifier les enjeux de qualité et de garantir l’accès à une information réellement fiable et pertinente pour chaque utilisateur.

Nos équipes accompagnent votre Data Office pour piloter et encadrer les usages de la donnée, assurant ainsi conformité, fiabilité et adoption. 

Grâce à des contrôles rigoureux, des indicateurs adaptés et une méthodologie éprouvée chez nos clients, nous couvrons l’ensemble du cycle de vie de la donnée : Data Management, MDM, qualité et catalogues de données.

Optimisez la gestion de vos données et renforcez votre stratégie d’entreprise en gouvernant la data par les usages, avec notre expérience reconnue.

Pour cela venez voir un de nos accélérateurs Data lors du prochain Salon Data & IA de Paris !

In data integration and data management, the focus is often on technology—databases, ETL processes, automation, reporting tools. But in the process, the true objective is easily overlooked: generating business value. 

Why does this happen? What organizational and technical barriers contribute to the disconnect? And most importantly: what strategies can we adopt to better align data initiatives with the goals of business stakeholders? 

This session explores the root causes and presents practical approaches to building a data-driven culture—with a clear focus on business impact.

Are you struggling to gain leadership support, craving stakeholder engagement, and begging for proper funding? Even though you may create Agentic AI wonders with your data, it won’t matter unless you explain the value in practical business terms. Join The Data Whisperer’s rollicking and riotous review of current buzzwords and some practical tips including:

• Differentiating between a data management narrative and other data storytelling and data literacy efforts

• Developing strategies to secure sponsorship and funding

• The 3Vs of Data Storytelling for Data Management

As AI reshapes every aspect of data management, organizations worldwide are witnessing a fundamental transformation in how data governance operates. This panel discussion, hosted by DataHub, brings together two forward-thinking customers to explore the revolutionary journey from traditional governance models to AI-autonomous systems. Our expert panelists will share real-world experiences navigating the four critical stages of this evolution: AI-assisted governance, where machine learning augments human decision-making; AI-driven governance, where algorithms actively guide policy enforcement; AI-run governance, where systems independently execute complex workflows; and ultimately, AI-autonomous governance, where intelligent systems self-manage and continuously optimize data stewardship processes. Through candid discussions of implementation challenges, measurable outcomes, and strategic insights, attendees will gain practical understanding of how leading organizations are preparing for this transformative shift. The session will address key questions around trust, accountability, and the changing role of data professionals in an increasingly automated governance landscape, providing actionable guidance for organizations at any stage of their AI governance journey.

Legacy data tools weren’t built for the AI era. Agentic Data Management replaces static rules and siloed platforms with intelligent agents that monitor, reason, and act—automating quality, governance, and lineage at scale. Discover how data leaders are shifting from manual firefighting to autonomous control, powering faster, trusted, and scalable data for AI and analytics.

- See a live demo of an agentic system in action

- Learn how probabilistic and deterministic approaches work in concert

- Explore how to build intelligent data products using the MCP protocol

Your AI is only as good as your data. Downtime, pipeline failures, and blind spots threaten revenue, compliance, and trust. Join Acceldata at Big Data London to explore Agentic Data Management (ADM), where AI agents autonomously resolve issues, optimize pipelines, and ensure governance. Powered by xLake Reasoning Engine, ADM delivers trusted, AI-ready data with self-healing operations. Hear how enterprises like Dun & Bradstreet boosted reliability and compliance. Ideal for data leaders, engineers, architects, analysts, product managers, and governance heads seeking autonomous data excellence. Visit Booth M70 for live demos

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

In this, the 10th year of Big Data LDN, in its flagship Great Dat Debate keynote panel, conference chair and leading industry analyst Mike Ferguson welcomes executives from leading software vendors to discuss key topics in data management and analytics. Panellists will debate the challenges and success factors in building an agentic enterprise, the importance of unified data and AI governance, the implications of key industry trends in data management, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, and issues on-the-horizon that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data and AI -driven enterprise from seasoned executives and an experienced industry analyst in a packed, unscripted, candid discussion.

For years, data engineering was a story of predictable pipelines: move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs.

This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.

Ten years ago, I began advocating for **DataOps**, a framework designed to improve collaboration, efficiency, and agility in data management. The industry was still grappling with fragmented workflows, slow delivery cycles, and a disconnect between data teams and business needs. Fast forward to today, and the landscape has transformed, but have we truly embraced the future of leveraging data at scale? This session will reflect on the evolution of DataOps, examining what’s changed, what challenges persist, and where we're headed next.

**Key Takeaways:**

✅ The biggest wins and ongoing struggles in implementing DataOps over the last decade. 

✅ Practical strategies for improving automation, governance, and data quality in modern workflows. 

✅ How emerging trends like AI-driven automation and real-time analytics are reshaping the way we approach data management. 

✅ Actionable insights on how data teams can stay agile and align better with business objectives. 

**Why Attend?**

If you're a data professional, architect, or leader striving for operational excellence, this talk will equip you with the knowledge to future-proof your data strategies.

The future of healthcare depends not only on breakthroughs in science, but also on how we harness the power of data, technology, and AI. To realise this future, we must challenge long-held assumptions about how data products are delivered. What once took months of complex engineering now happens in days—or even hours—by re-imagining the way we work. At AstraZeneca, we shifted from a traditional IT-centric model to one where business teams take ownership, rapid prototyping drives innovation, and automation ensures quality, compliance, and trust.

 This change is more than a process improvement; it is a cultural transformation. By aligning every step to business value, embracing bold goals, and learning from failure, we have built a system that empowers people to innovate at speed and at scale. Data products are no longer the end goal but the enablers of something greater: a knowledge fabric ready for AI, where enterprise context unlocks smarter decisions and accelerates the delivery of life-changing medicines.

Our journey proves that when ambition meets courage, and technology meets purpose, we can transform the way data serves science—and, ultimately, transform the lives of patients around the world.