• EP57 - From Hadoop & Hive to Minio & Dremio: Moving Towards a Next Gen Data Architecture

  • 2024/10/08
  • 再生時間: 1 時間 4 分
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EP57 - From Hadoop & Hive to Minio & Dremio: Moving Towards a Next Gen Data Architecture

  • サマリー

  • Legacy data platforms often fall short of the performance, processing and scaling requirements for robust AI/ML initiatives. This is especially true in complex multi-cloud (public, private, edge, airgapped) environments. The combined power of MinIO and Dremio creates a data lakehouse platform that overcomes these challenges, delivering scalability, performance and efficiency to ensure successful AI initiatives. Watch Brenna Buuck, Sr. Technical Evangelist at MinIO and Alex Merced, Sr. Technical Evangelist at Dremio provided insights on: - AI Workflows: How a data lakehouse simplifies critical AI tasks like model training, refinement, feature selection and real-time inference for faster decisions - Scalability and Performance: How a data lakehouse architecture scales seamlessly to meet the fast-growing demands of AI applications - Data Management Efficiency: How a data lakehouse streamlines data management for IT teams, allowing them to focus on innovation

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あらすじ・解説

Legacy data platforms often fall short of the performance, processing and scaling requirements for robust AI/ML initiatives. This is especially true in complex multi-cloud (public, private, edge, airgapped) environments. The combined power of MinIO and Dremio creates a data lakehouse platform that overcomes these challenges, delivering scalability, performance and efficiency to ensure successful AI initiatives. Watch Brenna Buuck, Sr. Technical Evangelist at MinIO and Alex Merced, Sr. Technical Evangelist at Dremio provided insights on: - AI Workflows: How a data lakehouse simplifies critical AI tasks like model training, refinement, feature selection and real-time inference for faster decisions - Scalability and Performance: How a data lakehouse architecture scales seamlessly to meet the fast-growing demands of AI applications - Data Management Efficiency: How a data lakehouse streamlines data management for IT teams, allowing them to focus on innovation

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