Building an Interoperable Data Lakehouse: Data Strategy for AI Leaders

Table of Contents Foreword: The AI Moment Demands a Connected Architecture3The Data Architecture Dilemma: Why Enterprises Are Stuck (orThinkThey Are)4Rethinking Architecture — from Data Silos to the Open, Interoperable Lakehouse6Connecting Data Without Compromise10 The AI Moment Demandsa Connected Architecture Businesses today are confronting the AI imperative, the pressure to findmeasurable value and efficiencies from using AI. They are looking beyondexperimentation and onto production, ultimately finding that successful AIdoesn’t really start with models — it starts with data. That is precisely why the way, a best-of-both-words architecture that combines the flexibility to empowereach data customer with their preferred setup, while maintaining central data approaches lack the foundational mechanism to let organizations realize fullagency over their data. Bidirectional interoperability means the ability tosecurely bring any engine to your data or access any data for both read and considerations. It involves standardizing metadata, lineage tracking and qualitychecks across all data sources, all in an effort to make data consistently clean,curated, labeled, accessible and governed across systems. But in practice, manybusinesses end up with a fragmented and duplicative data estate that spanssystems, clouds and regions, making it difficult to scale and near-impossibleto govern. It’s not unusual to see companies utilize different data warehouses, The Data Architecture Dilemma:Why Enterprises Are Stuck(or Think They Are) for the right pathway to get to the problem itself. In some cases, they just end up building a newpath, a short-term fix that often results in brittle, expensive and time-consuming pipelines that relyon copying data. That not only causes needless confusion as to the source of truth but it limits the processes to address their specific needs. Marketing and sales may turn toa data warehouse, while data science prioritizes a data lake. Adding to thecomplexity is the large ecosystem of enterprise SaaS tools, like Workday andSalesforce, all generating valuable data businesses need to power their AI proverbial firefighters, just hoping to keep pace with each new blaze before an all-out inferno breaksout. And with complex architectures that sprawl across multiple engines and multiple clouds, thesesystems can create tremendous operational drag. operational overhead or a managed solution that forces some degree of vendor lock-in. Either optioncomes with significant tradeoffs — most notably in reliability and cost. What legacy lakehouse approaches are missing is actually something attainable: a connectedyet open data and governance foundation that provides the flexibility for teams to choose their preferred engines and tools while centralizing governance and semantics. In this book, we will explore the open and interoperable data lakehouse — as a concept, its originsand some best practices designed to help enterprises succeed with AI, not just react to it.We will go over the three essential pillars holding up this architecture — bidirectional interoperability,streamlining for scale and universal governance for AI — all to demonstrate the near limitless power fewer headaches and lower costs. We’ll show how enterprises can gain full ownership of their dataestates again, freed from the fears of getting locked into that proverbial maze. Rethinking Architecture— from Data Silos tothe Open, InteroperableLakehouse Organizing it all can easily feel like a Sisyphean undertaking, endlessly managingdata warehouses for structured data alongside data lakes for unstructuredimages, videos or documents. In practice, this often created more silos andforced architects into complexity. But then, the lakehouse emerged to help This type of storage, capable of holding all sorts of data regardless of structure, is called a data lake. Data warehouse vs. lakehouse vs. data lake Then, there is the third type: the data lakehouse. To extend the metaphor, this garage has adjustablemetal racks along the walls and a few plastic bins that are well organized, but not everything fitsneatly into a box — and that’s OK. By and large, though, the garage is orderly, with each item having To help you understand the differences between traditional approaches to a lakehouse and aninteroperable lakehouse, we first need to review the three core architectures found in most datasystems today. Consider how people use their garages for storage. Think of those hyperorganizedfolks who have constructed built-in shelving along every wall and have organized all their belongings As one would imagine, each architecture has its own virtues (even the data lake — with its flexibilityand willingness to hold all kinds of stuff!), so it becomes important to assess which is right for anygiven circumstance. But what’s becoming clear for many forward-thinking organizations is that theinteroperable lakehouseis proving to be themodernoption that de