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Dr. Kimball explains how Hadoop can be both: A destination data warehouse, and also An efficient staging and ETL source for an existing data warehouse

Building a Hadoop Data Warehouse: Hadoop 101 for EDW Professionals Dr. Ralph Kimball explains how Hadoop can be both a destination data warehouse, and also an efficient staging and ETL source for an existing data warehouse. Learn how enterprise conformed dimensions can be used as the basis for integrating Hadoop and conventional data warehouses.

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.

A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse

In metadata, a data steward is a person that is responsible for maintaining a data element in a metadata registry. A data steward may share some responsibilities with a data custodian.

The Data Warehouse Lifecycle Toolkit, by Ralph Kimball et. el., Wiley, 1998, also briefly mentions the role of data steward in the context of data warehouse project management on page 70.

When considering implementing Operational BI solutions, many implementers first think of copying the operational data to an operational data store (ODS), data warehouse or data mart and analysing it there.  They are immediately faced with the problem of how to update the informational environment fast enough to satisfy the timeliness requirement of the users.  As that approaches real-time, traditional ETL tools begin to struggle.  Furthermore, in the case of the data warehouse, the question arises of the level of consistency among these real-time updates and between the updates and the existing content.  The way MongoDB is used points immediately to an alternative, viable approach--go directly against the operational data.

In the case of Operational BI, however, most experience indicates that the queries are usually relatively simple, and closely related to the primary access paths used operationally for the data concerned.

A cube can be considered a generalization of a three-dimensional spreadsheet.

Each cell of the cube holds a number that represents some measure of the business

OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system.

Common characteristics of a metadata registry A metadata registry typically has the following characteristics: Protected environment where only authorized individuals may make changes Stores data elements that include both semantics and representations Semantic areas of a metadata registry contain the meaning of a data element with precise definitions Representational areas of a metadata registry define how the data is represented in a specific format, such as in a database or a structured file format (e.g., XML)

ISO 11179

A metadata registry is a central location in an organization where metadata definitions are stored and maintained in a controlled method.

Fact tables are the foundation of the data warehouse. They contain the fundamental measurements of the enterprise, and they are the ultimate target of most data warehouse queries.

The grain is the business definition of what a single fact table record represents.

the grain is the description of the measurement event in the physical world that gives rise to a measurement.

A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts.

Dimensions are often recycled for multiple applications within the same database. For instance, a "Date" dimension can be used for "Date of Sale", as well as "Date of Delivery", or "Date of Hire". This is often referred to as a "role-playing dimension".

Seamless integration with “Big Data” using PolyBase.

Up to 50x performance gains with the xVelocity updateable columnstore.

The ADRM Software Healthcare Services Provider models consists of a set of integrated Enterprise, Business Area and Data Warehouse data models developed for organizations in the healthcare service provider industry. The focus of these models is to provide a blueprint of the complex data required to support a variety of applications, analytics and information services for health plan service providers. ·  Service Provider ·  Encounter ·  Claim ·  Patient ·  Service ·  Facility ·  Insurer ·  Health Plan