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RALPH KIMBALL DATA WAREHOUSE BOOK

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The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Third Edition Ralph Kimball founded the Kimball Group. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is. Kimball GroupThe Data Warehouse Toolkit, 3rd Edition - Kimball Group Warehouse and Business Intelligence Toolkit Books /; The Data Warehouse Toolkit, Ralph Kimball and Margy Ross co-authored the third edition of Ralph's classic.


Ralph Kimball Data Warehouse Book

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Over copies of the Toolkit books written by Ralph Kimball and the Kimball Group regarding data warehousing and business intelligence have been sold. RALPH KIMBALL, PhD, has been a leading visionary in the data warehouse 3 excellent books from the Kimball Group in their data warehouse toolkit series. Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of.

Loading in the data warehouse environment usually takes the form of presenting the quality-assured dimensional tables to the bulk loading facilities of each data mart. The target data mart must then index the newly arrived data for query performance. When each data mart has been freshly loaded, indexed, supplied with appropriate aggregates, and further quality 10 CHAPTER 1 assured, the user community is notified that the new data has been published.

Publishing includes communicating the nature of any changes that have occurred in the underlying dimensions and new assumptions that have been introduced into the measured or calculated facts. Data Presentation The data presentation area is where data is organized, stored, and made available for direct querying by users, report writers, and other analytical applications. Since the backroom staging area is off-limits, the presentation area is the data warehouse as far as the business community is concerned.

It is all the business community sees and touches via data access tools. This is what the presentation area with its dimensional models is all about. We typically refer to the presentation area as a series of integrated data marts.

A data mart is a wedge of the overall presentation area pie. In its most simplistic form, a data mart presents the data from a single business process.

These business processes cross the boundaries of organizational functions. We have several strong opinions about the presentation area. First of all, we insist that the data be presented, stored, and accessed in dimensional schemas.

The industry has concluded that dimensional modeling is the most viable technique for delivering data to data warehouse users. Dimensional modeling is a new name for an old technique for making databases simple and understandable. In case after case, beginning in the s, IT organizations, consultants, end users, and vendors have gravitated to a simple dimensional structure to match the fundamental human need for simplicity. Most people find it intuitive to think of this business as a cube of data, with the edges labeled product, market, and time.

We can imagine slicing and dicing along each of these dimensions. Points inside the cube are where the measurements for that combination of product, market, and time are stored. The ability to visualize something as abstract as a set of data in a concrete and tangible way is the secret of understandability.

The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling

If this perspective seems too simple, then good! A data model that starts by being simple has a chance of remaining simple at the end of the design. A model that starts by being complicated surely will be overly complicated at the end. Overly complicated models will run slowly and be rejected by business users. Dimensional Modeling Primer 11 Dimensional modeling is quite different from third-normal-form 3NF modeling.

Data is divided into many discrete entities, each of which becomes a table in the relational database. A database of sales orders might start off with a record for each order line but turns into an amazingly complex spiderweb diagram as a 3NF model, perhaps consisting of hundreds or even thousands of normalized tables. The industry sometimes refers to 3NF models as ER models.

ER is an acronym for entity relationship. Entity-relationship diagrams ER diagrams or ERDs are drawings of boxes and lines to communicate the relationships between tables. Both 3NF and dimensional models can be represented in ERDs because both consist of joined relational tables; the key difference between 3NF and dimensional models is the degree of normalization.

Normalized modeling is immensely helpful to operational processing performance because an update or insert transaction only needs to touch the database in one place. Normalized models, however, are too complicated for data warehouse queries. The use of normalized modeling in the data warehouse presentation area defeats the whole purpose of data warehousing, namely, intuitive and high-performance retrieval of data.

There is a common syndrome in many large IT shops. It is a kind of sickness that comes from overly complex data warehousing schemas.

Kimball Approach Overview

A dimensional model contains the same information as a normalized model but packages the data in a format whose design goals are user understandability, query performance, and resilience to change. Our second stake in the ground about presentation area data marts is that they must contain detailed, atomic data.

Atomic data is required to withstand assaults from unpredictable ad hoc user queries. While the data marts also may contain performance-enhancing summary data, or aggregates, it is not sufficient to deliver these summaries without the underlying granular data in a dimensional form.

In other words, it is completely unacceptable to store only summary data in dimensional models while the atomic data is locked up in normalized models. It is impractical to expect a user to drill down through dimensional data almost to the most granular level and then lose the benefits of a dimensional presentation at the final step.

In Chapter 16 we will see that any user application can descend effortlessly to the bedrock granular data by using aggregate navigation, but only if all the data is available in the same, consistent dimensional form.

We need the most finely grained data in our presentation area so that users can ask the most precise questions possible. All the data marts must be built using common dimensions and facts, which we refer to as conformed.

Adherence to the bus architecture is our third stake in the ground regarding the presentation area. Without shared, conformed dimensions and facts, a data mart is a standalone stovepipe application.

Isolated stovepipe data marts that cannot be tied together are the bane of the data warehouse movement. They merely perpetuate incompatible views of the enterprise. If you have any hope of building a data warehouse that is robust and integrated, you must make a commitment to the bus architecture. In this book we will illustrate that when data marts have been designed with conformed dimensions and facts, they can be combined and used together.

The data warehouse presentation area in a large enterprise data warehouse ultimately will consist of 20 or more very similar-looking data marts.

The dimensional models in these data marts also will look quite similar. Each data mart may contain several fact tables, each with 5 to 15 dimension tables. If the design has been done correctly, many of these dimension tables will be shared from fact table to fact table.

Dimensional Modeling Primer 13 Using the bus architecture is the secret to building distributed data warehouse systems. When the bus architecture is used as a framework, we can allow the enterprise data warehouse to develop in a decentralized and far more realistic way.

Data in the queryable presentation area of the data warehouse must be dimensional, must be atomic, and must adhere to the data warehouse bus architecture. If the presentation area is based on a relational database, then these dimensionally modeled tables are referred to as star schemas.

If the presentation area is based on multidimensional database or online analytic processing OLAP technology, then the data is stored in cubes. Dimensional modeling is applicable to both relational and multidimensional databases. Both have a common logical design with recognizable dimensions; however, the physical implementation differs. Fortunately, most of the recommendations in this book pertain, regardless of the database platform.

While the capabilities of OLAP technology are improving continuously, at the time of this writing, most large data marts are still implemented on relational databases.

In addition, most OLAP cubes are sourced from or drill into relational dimensional star schemas using a variation of aggregate navigation. For these reasons, most of the specific discussions surrounding the presentation area are couched in terms of a relational platform.

Contrary to the original religion of the data warehouse, modern data marts may well be updated, sometimes frequently. Incorrect data obviously should be corrected. Changes in labels, hierarchies, status, and corporate ownership often trigger necessary changes in the original data stored in the data marts that comprise the data warehouse, but in general, these are managed-load updates, not transactional updates.

Data Access Tools The final major component of the data warehouse environment is the data access tool s. We use the term tool loosely to refer to the variety of capabilities that can be provided to business users to leverage the presentation area for analytic decision making. Querying, obviously, is the whole point of using the data warehouse.

Ad hoc query tools, as powerful as they are, can be understood and used effectively only by a small percentage of the potential data warehouse business user population. The majority of the business user base likely will access the data via prebuilt parameter-driven analytic applications.

Approximately 80 to 90 percent of the potential users will be served by these canned applications that are essentially finished templates that do not require users to construct relational queries directly. Additional Considerations Before we leave the discussion of data warehouse components, there are several other concepts that warrant discussion. Metadata Metadata is all the information in the data warehouse environment that is not the actual data itself.

Metadata is akin to an encyclopedia for the data warehouse. Data warehouse teams often spend an enormous amount of time talking about, worrying about, and feeling guilty about metadata. Error Event Schema Subsystem 6: Audit Dimension Assembler Subsystem 7: Deduplication System Subsystem 8: Conforming System Delivering: Prepare for Presentation Subsystem 9: Slowly Changing Dimension Manager Subsystem Surrogate Key Generator Subsystem Hierarchy Manager Subsystem Special Dimensions Manager Subsystem Fact Table Builders Subsystem Surrogate Key Pipeline Subsystem Late Arriving Data Handler Subsystem Dimension Manager System Subsystem Fact Provider System Subsystem Aggregate Builder Subsystem Job Scheduler Subsystem Backup System Subsystem Recovery and Restart System Subsystem Version Control System Subsystem Version Migration System Subsystem Workflow Monitor Subsystem Sorting System Subsystem Lineage and Dependency Analyzer Subsystem He has remained steadfast in his long-term conviction that data warehouses must be designed to be understandable and fast.

Ralph's books on dimensional design techniques have become the all-time best sellers in data warehousing and he has trained more than 10, IT professionals around the globe. Ralph has his Ph. Kimball data warehousing: Data warehouses.

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Fact table Early-arriving fact Measure. Dimension table Degenerate Slowly changing. Business intelligence software Reporting software Spreadsheet.

Bill Inmon Ralph Kimball. Authority control BNF: Retrieved from " https: Hidden categories:This term dates back to the earliest days of relational databases.

Ralph Kimball

Sep 27, Harit Himanshu rated it it was amazing. Navigation Aids We have laced the book with tips, key concepts, and chapter pointers to make it more usable and easily referenced in the future.

It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. A useful introductory chapter describes the overall life cycle and principal pitfalls.