Episode 33 of The Data Business Podcast explores how enterprise data teams are shifting from custom one-off integrations to buying and selling data products through internal and external marketplaces. Lucas and Luna examine Snowflake's Marketplace, which now hosts over 2,000 third-party data listings and processes millions in monthly transactions, and compare it to AWS Data Exchange. They discuss why procurement teams are treating data like SaaS subscriptions, how data valuation is influencing contract terms, and what this means for data engineering roadmaps in 2026. The hosts also touch on the rise of data product managers and the tension between governance and speed. This episode is grounded in real examples and avoids hype — perfect for data leaders evaluating marketplace strategies.

#DataMarketplaces #DataProducts #Snowflake #AWSDataExchange #EnterpriseData #DataProcurement #DataEngineering #BusinessPodcast #FexingoBusiness #DataBusiness #DataValuation #DataGovernance #ProductMarketplace #SaaS #DataStrategy #Analytics #TechProcurement #DataInfrastructure

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Episode 32: Lucas and Luna dive into how data contracts are transforming enterprise software pricing. Through the lens of Snowflake's recent shift to consumption-based pricing and Databricks' new unit-based model, they explore why static subscription tiers are giving way to dynamic pricing tied to data volume and quality. They discuss the role of data contracts in enabling this shift, the challenges of unpredictable costs for customers, and what this means for CFOs and data teams in 2026.

#DataContracts #EnterprisePricing #Snowflake #Databricks #ConsumptionPricing #DataVolume #DataQuality #CFO #DataTeams #BusinessTechnology #DataInfrastructure #Podcast #FexingoBusiness #BusinessPodcast #DataBusiness #Analytics #CloudData #PricingModels

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Episode 31 explores a surprising shift in the data world: data catalogs are evolving from passive inventory tools into the active operating layer for enterprise data. Lucas unpacks how Databricks, Snowflake, and startups like Alation and Atlan are racing to build the 'control plane' for data, embedding governance, discovery, and lineage directly into the query path. Luna challenges whether this is just old metadata management rebranded. They dive into a concrete case: how a major European bank uses a catalog to reduce data incident response time from hours to under five minutes. The episode examines the technical and organisational forces pushing this shift, including the rise of data mesh and lakehouse architectures, and asks whether the catalog is finally becoming the single source of truth companies have been chasing for two decades.

#DataCatalog #EnterpriseData #MetadataManagement #DataGovernance #DataMesh #Lakehouse #Databricks #Snowflake #Alation #Atlan #DataLineage #DataDiscovery #BusinessTechnology #DataEngineering #DataInfrastructure #DataStrategy #FexingoBusiness #BusinessPodcast

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Episode 30 of The Data Business Podcast. Lucas and Luna explore the emerging discipline of data valuation and how it's reshaping mergers and acquisitions. They dive into the 2025 acquisition of a mid-market AI firm where the target's synthetic data library was valued at $47 million — more than its software stack. The hosts discuss the SEC's evolving stance on data as a balance-sheet asset, the accounting firm battle over valuation standards, and why private equity is hiring data economists. If you work in data or M&A, this episode gives you a framework for thinking about what data is actually worth.

#DataValuation #MergersAndAcquisitions #DataEconomy #DataAssets #SyntheticData #BusinessValuation #PrivateEquity #SEC #DataAccounting #DataLibraries #FinancialReporting #DataEconomics #EnterpriseData #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataBusiness #DataDriven

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In this episode of The Data Business Podcast, Lucas and Luna explore how data lakehouses are quietly resolving the long-standing war between data warehouses and data lakes. Anchored by the recent Databricks acquisition of Arcion for $100 million, they unpack what the lakehouse architecture actually is, why enterprises are adopting it, and how it changes the economics of data infrastructure. They walk through a concrete example: a mid-size e-commerce company migrating from a Snowflake warehouse and an S3 data lake onto a unified lakehouse platform, cutting their total data infrastructure costs by 38 percent while reducing time-to-insight from hours to minutes. The conversation also touches on how lakehouses affect data engineering roles, vendor lock-in concerns, and why this might be the last major architectural shift in enterprise data for a while. If you're building or running a data team, this is the architectural decision you can't ignore.

#DataLakehouse #Databricks #Arcion #DataArchitecture #DataWarehouse #DataLake #EnterpriseData #DataInfrastructure #DataEngineering #DeltaLake #Snowflake #ApacheIceberg #OpenTableFormats #DataGovernance #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataBusiness

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Lucas and Luna explore the hidden problem of data leakage in machine learning — when information from the future accidentally leaks into training data, inflating model accuracy and causing catastrophic failures in production. They examine a specific case: a major retail bank that launched a fraud detection model showing 98 percent accuracy in testing, only to see it fail in the real world because the data pipeline had inadvertently included transaction timestamps and future labels. The episode breaks down the three most common types of leakage — target leakage, train-test contamination, and feature leakage — and explains how companies like Uber and Airbnb have built systems to detect it. Lucas shares the one metric engineering teams should monitor, and Luna presses on why most organizations don't catch leakage until it's too late.

#DataLeakage #MachineLearning #FraudDetection #MLEngineering #DataScience #FeatureEngineering #ModelValidation #Airbnb #Uber #RetailBanking #ProductionML #DataPipelines #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure #MLOps #DataQuality

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Podcast hosts Lucas and Luna explore how data clean rooms are transforming the way companies share customer data without compromising privacy. This episode focuses on a specific case: a major retailer and a consumer packaged goods brand using a clean room to run joint audience analysis while keeping raw data siloed. Lucas explains the technical architecture — differential privacy, query constraints, and audit logs — while Luna questions whether this is just a workaround for consent fatigue. They discuss the $5 billion market opportunity projected by 2028, the role of cloud providers like Amazon and Google in offering clean room services, and why companies like Snowflake and LiveRamp are betting big on this infrastructure. The hosts also touch on regulatory tailwinds from GDPR and CCPA that are pushing advertisers toward these privacy-safe environments. If you work in data engineering, advertising, or privacy compliance, this episode offers a concrete look at a technology that is quietly becoming standard practice.

#DataCleanRooms #Privacy #Advertising #DifferentialPrivacy #GDPR #CCPA #Snowflake #LiveRamp #Amazon #Google #RetailMedia #CustomerData #DataInfrastructure #Business #Technology #FexingoBusiness #BusinessPodcast #DataEngineering

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Lucas and Luna unpack the rise of data version control (DVC) as a critical infrastructure layer for enterprise AI. They explore how tools like DVC and LakeFS are bringing Git-like versioning to datasets, enabling reproducibility, auditability, and collaboration at scale. The hosts walk through a concrete example: how a pharmaceutical company used data version control to track training data for a drug discovery model across 40 versions, cutting debugging time from weeks to hours. They discuss why traditional data warehouses and data lakes fail to handle the versioning needs of modern machine learning pipelines, and how data version control is becoming as essential as source control for code. Luna challenges Lucas on the overhead of maintaining yet another tool in the stack, and Lucas argues that the cost is outweighed by the gains in model governance and regulatory compliance. The episode also covers how data version control integrates with feature stores and data observability, and why it's becoming a standard requirement in MLOps platforms. A must-listen for data engineers, ML practitioners, and anyone building AI systems that need to be reproducible and auditable.

#DataVersionControl #DVC #LakeFS #EnterpriseAI #MLOps #DataEngineering #MachineLearning #Reproducibility #DataGovernance #DataLineage #FeatureStores #ModelObservability #PharmaceuticalAI #DrugDiscovery #DataInfrastructure #BusinessAndTechnology #FexingoBusiness #BusinessPodcast

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When a major European bank discovered that its data lineage was manually tracked in spreadsheets, the compliance team spent 14 months untangling a single loan-origination pipeline. This episode examines how graph databases are replacing traditional catalog tools for data governance, using a real case from a financial institution that cut policy-audit time from weeks to hours. Lucas and Luna break down why property graphs outperform relational models for lineage, how Neo4j and Amazon Neptune are competing in this niche, and what the rise of data-contract enforcement means for engineering teams building governance automation. They also discuss the tension between open standards like OpenLineage and vendor lock-in, and why the graph approach is especially relevant for organizations dealing with GDPR and AI-regulation requirements. If you're evaluating data-governance tooling or building policy-as-code infrastructure, this episode offers a concrete framework for when graph technology makes sense and when it doesn't.

#DataGovernance #GraphDatabases #DataLineage #PolicyEnforcement #Neo4j #AmazonNeptune #OpenLineage #GDPR #DataContracts #DataEngineering #ComplianceAutomation #BusinessTechnology #Business #DataArchitecture #DataPolicy #FexingoBusiness #BusinessPodcast #DataBusinessPodcast

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Episode 24 of The Data Business Podcast: Lucas and Luna explore the shift from data-as-a-service to data product thinking in enterprise procurement. They break down how Snowflake's Marketplace and Databricks' Delta Sharing are turning raw data into packaged, priced products with SLAs. The conversation focuses on the concept of 'data product managers' and how companies like Fidelity and JPMorgan are restructuring teams around product-based data delivery. Lucas explains the pricing shift from per-GB to per-query and outcome-based models, citing Snowflake's consumption pricing and the rise of data contracts. Luna challenges whether this is just rebranding, and Lucas counters with evidence of real procurement changes. The episode ends with a look at how data product thinking affects vendor negotiation and internal team dynamics.

#DataProducts #EnterpriseData #DataProcurement #Snowflake #Databricks #DeltaSharing #DataMarketplace #DataContracts #ConsumptionPricing #DataProductManager #Fidelity #JPMorgan #DataAsAProduct #DataMesh #BusinessAndTechnology #BusinessPodcast #FexingoBusiness #DataEngineering

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