Prasad Pore

The lakehouse architecture is becoming the cornerstone of modern data management for analytics and AI, yet it faces design challenges due to multiple interpretations of the purpose of data zones or medallion layers. This session provides clarity on the purposes and the design principles of the lakehouse architecture.

We are at the start of a massive, AI-driven feedback loop. A loop between a universal language, Python, a universal engine, Spark, and universal storage, Open Table Formats, that will accelerate us from simple automation to fully agentic, automated data management. This session helps D&A leaders assess their strategy for navigating this disruptive transition and its opportunities and risks.

Almost every GenAI use case requires organizations to extract, qualify and govern significant volumes of unstructured data. Data management leaders must deliver workflows that orchestrate entity extraction, vector data embeddings and semantic data enrichment with structured data pipelines to deliver GenAI-ready data. Join this session to learn more.

私たちは今、大規模なAI主導のフィードバック・ループの出発点にいる。このループは、ユニバーサル言語であるPython、ユニバーサル・エンジンであるSpark、さらにユニバーサル・ストレージであるオープン・テーブル・フォーマットの間で構成されており、シンプルな自動化から完全にエージェント化され、自動化されたデータ管理へと我々を加速させるものである。本セッションでは、D&Aリーダーがこの破壊的な変革に対処するための戦略と、その機会およびリスクを評価する上で役立つインサイトを提供する。

The lakehouse architecture is becoming the cornerstone of modern data management for analytics and AI, yet it faces design challenges due to multiple interpretations of the purpose of data zones or medallion layers. This session provides clarity on the purposes and the design principles of the lakehouse architecture.

Data lakehouses continue to be hyped, but do they replace or complement data lakes and data warehouses? Where do we stand from an architectural perspective? What is hype, and what is real? What should be expected in the coming years?