数据湖仓分析与AI：为AI新时代设计企业分析体系

LAKEHOUSEESSENTIAL GUIDE ANALYTICSAND AI Designing enterprise analytics for the new era of AI TABLE OF CONTENTS The Imperative of Lakehouse Analytics in the Age of AI3The Open Lakehouse: Storage, Catalog and Compute4Architecting a Resilient Lakehouse Analytics and AI Practice6Common Pitfalls of Traditional Lakehouse Solutions8Snowflake for Lakehouse Analytics and AI10Charting Your Course: A Practical Transition Strategy12Conclusion: From Data to Impact13 THE IMPERATIVE OFLAKEHOUSE ANALYTICS IN THE AGE OF AI A lakehouse architecture untangles storage, catalog and compute, providing theflexibility to choose the right tools for each team. The emergence of ApacheIceberg™ as the leading vendor-neutral and interoperable open table format hasaccelerated this trend by making it easier to bring tools to your data, rather thandata to your tools. The result is greater data democratization by empoweringorganizations to rapidly adopt new tools, drive faster innovation, and scaleanalytics and AI initiatives all via a single copy of data and without being lockedinto specific vendors or complex architectures. For data leaders responsible for shaping their organization’s future — architects,CIOs and CDOs — the strategic challenge is no longer just about managingdata. It’s about unifying a vast and varied data estate to power today’s businessintelligence and AI-driven innovation. The answer for many forward-thinking organizations with an in-house dataengineering team is the open lakehouse. This modern architectural approachpromises to deliver the best of two worlds: the performance and governanceof a traditional data warehouse combined with the flexibility and scaleof a data lake. But adopting a lakehouse architecture is only the first step. To truly unlockits potential, you must power it with an analytics platform that can meetthe demands of the AI era. This guide introduces a strategic framework forevaluating a lakehouse analytics solution that delivers on the promise ofopenness without compromising on performance, security or reliability. It isdesigned to help you make sense of shifting requirements, understand what aworld-class solution looks like, and frame a productive conversation with yourinternal stakeholders. A lakehouse architecture untangles storage, catalog and compute,providing the flexibility to choose the right tools for each team. THE OPEN LAKEHOUSE:STORAGE, CATALOGAND COMPUTE At its core, a lakehouse architecture is defined by the separation of three key components: storage, catalogand compute. Understanding how these layers interact when standardizing on Iceberg tables is fundamentalto building a flexible and powerful data foundation. The Iceberg catalog layer The storage layer An Iceberg catalog serves as the metastore and the authoritativesource of truth for all data in your table layer. Instead of storingall table metadata internally, the catalog maintains a pointerto the current metadata file for each table. This file containsthe complete snapshot history, schema, partition spec and filemanifests describing where the actual data files are stored. Byupdating this pointer atomically — using a compare-and-swapoperation — the catalog enables ACID transactions (atomicity,consistency, isolation, durability), providing data reliability andpreventing corruption across concurrent operations. For broadinteroperability, catalogs should implement the Iceberg RESTCatalog Specification, a standard API that lets any compliantengine (such as Snowflake, Trino, Spark or Flink) interactconsistently with your Iceberg tables. Choosing a catalog thatadheres to this specification is essential for maintaining a single,governed copy of data. The storage layer is the foundation of the lakehouse, usinglow-cost, highly scalable cloud object storage (like Amazon S3,Google Cloud Storage or Azure Data Lake Storage) to hold alldata — structured, semistructured and unstructured — in itsraw or transformed state. Apache Iceberg™ is the leading opentable format for semistructured and structured data, deliveringcritical capabilities like schema evolution, partitioning andtransaction management. Its broad support across engines andtools provides the essential foundational flexibility that allowsyou to select the right catalog and compute layers for yourlakehouse architecture. The compute layer This is where the work happens. The compute layer consistsof one or more engines — for SQL analytics, data engineeringor model training and inference — that query and process datafrom the storage layer by interacting with the catalog. For anyread operation, the engine accesses the catalog to get the table’scurrent metadata file, which it uses to plan the query and fetchdata files directly from object storage. For write operations, theengine writes new data files, creates new metadata, and thenuses an atomic “compare-and-swap” operation to ask the catalogto update the pointer, preserving transactional integrity. Thisclear separation