Paperback: 152 pages
Publisher: Apress; 1st ed. edition (March 20, 2015)
Product Dimensions: 6.1 x 0.4 x 9.2 inches
Shipping Weight: 10.6 ounces (View shipping rates and policies)
Average Customer Review: 4.9 out of 5 stars See all reviews (9 customer reviews)
Best Sellers Rank: #139,318 in Books (See Top 100 in Books) #16 in Books > Computers & Technology > Programming > Languages & Tools > Compilers #141 in Books > Textbooks > Computer Science > Database Storage & Design #350 in Books > Computers & Technology > Databases & Big Data
This book provides a unique and valuable framework for Window functions in T-SQL (SQL Server). The material is relevant for people doing analysis through advanced analytics. Window functions provide a way to generate multiple features (aka variables, attributes) for use in data mining and machine learning. In this review, I provide a brief description of the topics and pay special attention to areas which I believe are important for data scientists (in their data preparation for further advanced analytics).The chapters:Chapter 1: Looking Through the Window: the basics on OVER and PARTITION BY, including tricky ways to handle ROW_NUMBER()Chapter 2: Discovering Ranking Functions: ROW_NUMBER(), RANK, DENSE RANK, and NTILE. Many data scientists will have use for NTILE. I created an advanced concept based on NTILE for a client last year.Chapter 3: Summarizing with Window Aggregates: Classic statisticians will enjoy having these statistical functions including AVG and STDEV. Only trained statistical people can explain the difference between STDEV and STDEVP (similar to Excel’s functions).Chapter 4: Tuning for Better Performance: Looking at the execution plans is important. There are often ways to refactor code to achieve the same result in less time. This type of activity is important for “big data”, tables or joins which are large in size. These performance-measuring techniques are also the way to measure the merit of window functions over other ways to accomplish these same results in SQL Server.Chapter 5: Calculating Running and Moving Aggregates: ORDER BY allows for creating a metric based on a subset. Any of the statistical functions can be used for these metrics, not just COUNT but MAX and others too.
Not for first-timers on T-SQL - starting out on this book without even a clue regarding the GROUP BY clause would make for rough seas for a rank beginner. This book is squarely aimed at T-SQL'ers interested in the new "row windowing" features introduced in SS2K5 and SS2K12 who need getting up-to-date following important advances in SQL Server.This book delivers. In addition to being straightforward and not hard to understand, the authors systematically hit the nail right on the head.Right off the bat, the authors provide a strong motivation of windowing functions by demonstrating their power with a simple example showing how to list daily stock exchange quotes along with the closing value from the previous day. Not only is the proper solution given using the LAG function, that solution is compared with the older approach of using a derived table from a correlated sub-query. The performance improvement is clearly highlighted.Further motivation is provided by the "isolated islands" problem, i.e. showing contiguous ranges and gaps in a consecutive series. Remembering the ugly code I wrote when I had just a couple of years of experience with T-SQL, the solution given is so much cleaner.Not only is the proper usage of windowing functions explained in detail, their limitations are also given. There is also a chapter on tuning the queries to improve performance.In addition the authors have made available on the web all the source code, which enables experimentation of the solutions given without the drudgery of typing in the code in SSMS. Multiply the time savings by the number of T-SQL'ers trying out the solutions provides an indication to the value offered to the community.
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