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Bookbot

Bill Inmon

    Data Lake Architecture
    The Unified Star Schema
    DILIGENTLY SCREAMING ASCENSION RISE IN T
    • A foreboding power begins to rise Allies turn foe And hearts begin to tremble In a land of chaos and ruin marked by an evil mages rein, a few brave souls stand against a tyrant. Luminous and Aurora must lead their company across unfamiliar terrain to find the Legacy Stone fragments and repair their sacred stone of power before its too late. Through a perilous journey of love, loss, and triumph, the company is forced to bear harsh elements, traps, and a host of enemies, and where knowing who to trust is only half the battle. While death and turmoil linger, shadows begin to rise in the companys hearts, forged from the despair of darkness

      DILIGENTLY SCREAMING ASCENSION RISE IN T
      4,4
    • The Unified Star Schema

      An Agile and Resilient Approach to Data Warehouse and Analytics Design

      • 294 Seiten
      • 11 Lesestunden

      Master the most agile and resilient design for building analytics applications: the Unified Star Schema (USS) approach. The USS has many benefits over traditional dimensional modeling. Witness the power of the USS as a single star schema that serves as a foundation for all present and future business requirements of your organization.

      The Unified Star Schema
      3,5
    • Data Lake Architecture

      Designing the Data Lake and Avoiding the Garbage Dump

      • 166 Seiten
      • 6 Lesestunden

      Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps.Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake metadata, integration mapping, context, and metaprocess.Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.

      Data Lake Architecture