TDWI Checklist Report: Cloud Data Warehousing

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tdwi.org 3 TDWI RESE ARCH T D W I C H E C K L I S T R E P O R T: C LO U D DATA WA R E H O U S I N G S T R A I G H T TA L K 1 • End-user querying, reporting, and analysis tools enabling data consumers to gather actionable knowledge to motivate profitable actions In essence, a data warehouse is not a system or even a platform. Rather, TDWI defines a data warehouse as a data architecture populated with data, metadata, and schema unified via an integration backbone. This distinction is important because traditional data warehouses typically were implemented as monolithic systems, specialized "analytical appliance" hardware platforms that depreciate in both performance and value over time. Fortunately, the cloud provides an alternative architecture that eliminates limitations of rigid, traditional on-premises implementations. To set the stage for considering a cloud-based reporting and analytics environment, we need a working definition of a data warehouse. Conceptually, a data warehouse is a centralized repository of data organized to simplify data reporting, analytics, and production of actionable knowledge to drive advantageous and beneficial decision making. Practically, though, a data warehouse is a database implemented as a segregated data architecture in which the data is organized using a schema that simplifies and accelerates reporting and analytics. The data schema often facilitates aggregation and summarization. Data consumers can easily execute queries providing insight associated with aggregation along various dimensions of classification such as time and geography, as well as other defined cat - egorizations. The data warehouse is populated on a periodic basis with data sets that are extracted from internal application systems as well as data sets acquired from other sources. The data warehouse is often supported with additional capabilities and services, such as: • A metadata repository documenting the data elements managed within the data warehouse • Identified source systems from which data sets are extracted to load into the data warehouse • Processes for cleaning and standardization to prepare data for loading into the data warehouse • Data integration processes that execute the transformations and load the data • Processes for filtering data by subject area and reducing data volumes into data marts UNDERSTANDING THE CHARACTERISTICS OF DATA WAREHOUSES

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