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1、The data store for AIContents01 Introduction02 The current state of data architecture03 The data lakehouse defined04 Components of the architecture05 Cost optimizationopportunities06 Analytics and datascience enhancements07 IBM watsonx.data08 Next steps3Next chapterPrevious chapter01 IntroductionThi

2、s ebook will examine the latest open data management solution for data and analytics leaders who want to significantly reduce cost,simplify data access and automate unified governance to scale AI.Its time for the data lakehouse.Data is at the center of every business.It keeps applications running,po

3、wers predictive insights and enables better experiences for customers and employees.But the full benefit of data is elusive because of the way that data is stored and accessed for analytics and AI.Youre not alone if you rely on monolithic repositories with multiple data warehouses and data lakes,on

4、premises and on cloud;82%of organizations are inhibited by data silos.1 And its about to get worse:according to IDC,the amount of stored data is expected to grow 250%by 2025.2The data lake was supposed to fix all these issues;just land your data in a centralized place and process it.But its not so e

5、asy to update the lakes,properly catalog data or ensure good governanceand the skillsets required for these tasks are specific,rare and expensive.As a result,data lakes have proven costly to build and maintain.A data warehouse does offer high performance for processing terabytes of structured data.B

6、ut warehouses can become expensive,too,especially for new and evolving workloads.Most organizations run analytics and AI workloads in ecosystems that are complex and cost inefficient.Its time for a change.4Next chapterPrevious chapter250%The amount of storeddata is expected togrow 250%by 2025.25Next