『The Data Business Podcast with Fexingo: Analytics, Data Infrastructure, and Information Products』のカバーアート

The Data Business Podcast with Fexingo: Analytics, Data Infrastructure, and Information Products

The Data Business Podcast with Fexingo: Analytics, Data Infrastructure, and Information Products

著者： Fexingo
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Data is the raw material of modern business, but most companies drown in it. The Data Business Podcast with Fexingo examines how organizations turn data into durable products and infrastructure — from analytics stacks and data pipelines to information platforms that generate recurring revenue. Lucas and Luna dissect real cases: how Snowflake built a cloud-data monopoly, why dbt became the standard for transformation, and how startups like Fivetran and Airbyte compete in the extraction market. They explore the economics of data-marketplaces, the governance trade-offs of lakehouse architectures, and the metrics that separate high-performing data teams from compliant ones. Each episode grounds a specific tension — open-source vs. proprietary, speed vs. accuracy, self-service vs. centralization — in the numbers and decisions that matter. Designed for data engineers, analytics leaders, and product managers building data-intensive businesses, the show avoids hype and focuses on the durable principles that survive tool churn. Lucas brings a journalist's precision to the business models behind the stack; Luna challenges with practitioner questions about real-world friction. By the end, you'll understand not just what tools are trending, but why the economics of data are shifting — and what that means for your next build-or-buy decision. #DataBusiness #Analytics #DataInfrastructure #DataEngineering #InformationProducts #Snowflake #Dbt #Fivetran #Airbyte #Lakehouse #DataGovernance #DataMarketplace #OpenSourceData #DataMonetization #Business #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo© 2026 Fexingo. All rights reserved. 経済学
エピソード
  • How Data Contracts Cut Enterprise Integration Costs

    How Data Contracts Cut Enterprise Integration Costs
    2026/06/08
    Data contracts—agreed-upon schemas and SLAs between data producers and consumers—are quietly reshaping how large companies manage data pipelines. This episode unpacks why enterprises are moving from informal data handoffs to legally-binding data contracts, using examples from a major European retailer that cut integration time by 40% after adopting them. We explain how data contracts reduce miscommunication, enforce data quality at the source, and create a shared language for data teams and business stakeholders. Lucas and Luna also explore the trade-offs: the upfront investment in defining contracts, the risk of over-engineering, and whether this approach scales beyond large tech-forward firms. For anyone building or managing data infrastructure, this is a practical look at a trend that's moving from niche to mainstream. #DataContracts #DataGovernance #DataEngineering #EnterpriseData #DataIntegration #DataQuality #SchemaManagement #DataObservability #DataProducts #DataLineage #DataMesh #DataCatalog #Business #Technology #FexingoBusiness #BusinessPodcast #DataInfrastructure #DataOps Keep every episode free: buymeacoffee.com/fexingo
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    9 分
  • Why Data Teams Are Adopting Data Contracts Over APIs

    Why Data Teams Are Adopting Data Contracts Over APIs
    2026/06/08
    Episode 38 of The Data Business Podcast: Lucas and Luna dig into why a growing number of enterprise data teams are replacing traditional REST APIs with data contracts — formal agreements between data producers and consumers that specify schema, semantics, SLAs, and expected usage. They unpack a real case from a mid-sized fintech that cut downstream incidents by 40 percent after switching to contract-driven data sharing. Lucas explains how data contracts differ from both APIs and data catalogs, why they reduce integration costs for analytics and AI pipelines, and where the approach still falls short. Luna questions whether the model scales across thousands of datasets and brings in a counterexample from a consumer-goods company that tried and reverted. The episode includes a listener-support mention for keeping the show ad-free. Tune in for a focused comparison between two approaches to data exchange. #DataContracts #API #DataIntegration #DataGovernance #DataProduct #Fintech #SchemaEvolution #SLA #DataProducers #DataConsumers #Business #Technology #DataEngineering #DataPipelines #Analytics #AI #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo
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    10 分
  • How Feature Stores Are Solving the Data Science Bottleneck

    How Feature Stores Are Solving the Data Science Bottleneck
    2026/06/07
    Feature stores have quietly become one of the most important pieces of data infrastructure for machine learning teams. Lucas and Luna explore how companies like Uber, Airbnb, and DoorDash use feature stores to avoid duplicating work, reduce time-to-deployment, and operationalize ML at scale. They dive into the specific problem of 'training-serving skew', the rise of open-source tools like Feast, and why feature stores are becoming a standard part of the enterprise data stack. Along the way, they discuss how feature stores connect to data version control and data contracts, and what this means for data teams building production ML systems in mid-2026. If you're a data engineer, ML engineer, or technical manager wondering whether a feature store is worth the investment, this episode gives you the concrete use cases and trade-offs to make that call. #FeatureStore #MachineLearning #DataEngineering #MLInfrastructure #Uber #Airbnb #DoorDash #Feast #TrainingServingSkew #FeatureEngineering #DataScience #MLOps #DataVersionControl #DataContracts #EnterpriseData #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo
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    9 分
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