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Product Description
Product Description
- Cowritten by Ralph Kimball, the world′s leading data warehousing authority, whose previous books have sold more than 150,000 copies
- Delivers real–world solutions for the most time– and labor–intensive portion of data warehousing–data staging, or the extract, transform, load (ETL) process
- Delineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouse
- Offers proven time–saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality
From the Back Cover
The extract, transform, and load (ETL) phase of the data warehouse development life cycle is far and away the most difficult, time–consuming, and labor–intensive phase of building a data warehouse. Done right, companies can maximize their use of data storage; if not, they can end up wasting millions of dollars storing obsolete and rarely used data. Bestselling author Ralph Kimball, along with Joe Caserta, shows you how a properly designed ETL system extracts the data from the source systems, enforces data quality and consistency standards, conforms the data so that separate sources can be used together, and finally delivers the data in a presentation–ready format.
Serving as a road map for planning, designing, building, and running the back–room of a data warehouse, this book provides complete coverage of proven, timesaving ETL techniques. Beginning with a quick overview of ETL fundamentals, it then looks at ETL data structures, both relational and dimensional. The authors show how to build useful dimensional structures, providing practical examples of techniques.
Along the way you’ll learn how to:
- Plan and design your ETL system
- Choose the appropriate architecture from the many possible options
- Build the development/test/production suite of ETL processes
- Build a comprehensive data cleaning subsystem
- Tune the overall ETL process for optimum performance
Ideally, you must start the design of your ETL system with one of the toughest challenges: surrounding the requirements. Read the first page
Concordance
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews
Here's a sample sentence: "This section discusses what needs to go into the data-cleansing baseline for the data warehouse, including simple methods for detecting, capturing and addressing common data-quality issues and procedures for providing the organisation with improved visibility into data-lineage and data-quality improvements over time". Now imagine a whole book written like this. OK, I've taken this sentence out of context, but if I tell you that this was used to introduce a section - there are no preceding or trailing sentences - then I think I am starting to paint a picture.
The authors and publishers seem to have taken the attitude, "Why use a bullet point when a paragraph will do?". Text and examples have been embellished as if in an effort to prove how clever the authors are. A lot of jargon is employed (no glossary), but the reader is always left in doubt as to whether this is industry standard or idiom employed only by the authors.
I think this book could have been so much more useful if they had taken a worked example right through from start to finish. They could have explained where the real world may be different to this perfect model and drawn on their experiences to add colour. Also, if this truly was supposed to be a book of practical techniques, they should have highlighted them, say 1 to 100, through the text, as applicable.
So why two stars rather than none? Firstly, because there are some good nuggets of information in there, if you work hard to find them, and secondly, because the authors' interest for the subject does show through. They do have a knack of answering the important questions, but only after a long journey round the houses first.
Kimball and Caserta would probably be fantastic consultants to have on a big data warehousing project, unfortunately they are awful technical writers - only buy this book if nothing else covers the subject you are interested in.
However, I'd say the REAL reason for buying this book is it works well with Ralph Kimballs other work "The Data Warehouse Toolkit", and gives an excellent summary of Dimensional Design. I guess the authors felt they must put this in to explain the background. Personally I found it invaluable.
Also the description of "real time ETL" was invaluable. Everyone's talking about it, but the book gives a credible outline solution.
Yes woolly, yes it uses 10 words when two would do, but overall I got a lot out of it.
Recommended.
Also like the other books, this one takes a vendor agnostic approach. While this may increase the shelf-life of the book, I would have appreciated some comparisons between the major vendors out there today.
Overall: I recommend this one as a buy, even if you have Ralph's other books.
The book is relevent for people using most ETL or ELT tools and it will remain relevent for years even as the ETL products continue to advance and mature. It is targeted at DW but the basic flow of Extract, Clean, Conform and Deliver is suitable for most types of data loads.
Good coverage of the alternatives to traditional overnight bulk loads in the section on real-time ETL systems (also describes Microbatch) as the businesses and the major ETL vendors shift to SOA.
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