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Databricks CEO claims LTAP cracks 40-year database divide for AI

By PulseAugur Editorial · [1 sources] ·

Databricks has introduced a new architecture called LTAP (Lake Transactional/Analytical Processing) aimed at unifying separate transactional (OLTP) and analytical (OLAP) data systems. CEO Ali Ghodsi argues that the long-standing divide, which has persisted across various database technologies like Oracle and Snowflake, is a significant bottleneck for enterprise AI. LTAP proposes operating both transactional and analytical engines on a single copy of data stored in low-cost cloud object storage, eliminating the need for data pipelines and stale copies. AI

IMPACT This unification could streamline data access for AI agents, potentially accelerating AI development and deployment by removing infrastructure bottlenecks.

RANK_REASON Databricks CEO announces a new architecture that claims to solve a long-standing industry problem. [lever_c_demoted from significant: ic=1 ai=0.7]

Read on Forbes — Innovation →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Databricks CEO claims LTAP cracks 40-year database divide for AI

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Databricks CEO announces a new architecture that claims to solve a long-standing industry problem. [lever_c_demoted from significant: ic=1 ai=0.7]
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COVERAGE [1]

  1. Forbes — Innovation TIER_1 English(EN) · Victor Dey, Contributor ·

    Databricks CEO Says He’s Cracked A 40-Year-Old Database Problem With LTAP

    At Data + AI Summit, Databricks CEO Ali Ghodsi unveiled LTAP, a new architecture that collapses the 40-year unification problem of OLTP and OLAP databases.

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