Every story tagged Data Lakehouse, curated for CIOs and IT leaders — ranked by source credibility, engagement, and freshness.
2 stories · open in the command center
Data lakehouses have become the standardized enterprise architecture for analytics and AI, combining data warehouse structure with data lake flexibility to support diverse workloads from business intelligence to generative AI applications. The emergence of open standards like Apache Iceberg reduces vendor lock-in and enables seamless integration with third-party enterprise systems, allowing organizations to democratize data access through AI interfaces while providing critical context for LLMs through RAG embeddings and multi-turn conversations. For IT organizations, this shift represents both an architectural opportunity to consolidate fragmented data systems and a strategic imperative to prepare infrastructure that supports increasingly AI-driven analytics and enterprise agents.
SAP's acquisition of Dremio strengthens its AI and data strategy by adding a lakehouse platform that enables enterprises to unify SAP and non-SAP data without moving it to external systems, addressing a critical barrier to enterprise AI success where most projects fail due to fragmented and inaccessible data rather than AI limitations. This move positions SAP to compete more effectively in agentic AI by solving the data preparation and accessibility challenges that plague predictive analytics and AI-driven decision-making, particularly for regulated enterprises that cannot easily export data to cloud platforms. While Dremio is less mature than existing SAP partners Snowflake and Databricks, the acquisition signals rapid convergence between ERP and lakehouse technologies and should prompt IT leaders to reassess their data architecture and AI readiness strategy.