- Why do we need to transform data?
We need to make data consistent and prepared for our analytical purposes. Data can come from a range of data sources with very volatile quality. - Is it always good approach to consume source data directly?
Sometimes, yes, but not always. One of the reasons why we should stage data aside is to avoid conflicts with common data contention coming to a source data. - Why is the OPENQUERY function preferred when writing distributed queries?
The first reason is that security when using the OPENROWSET function is compromised. The second reason is to do with performance. Distributed queries written using the OPENQUERY function often perform better than those written using an ad hoc approach. - Can we modify data using views?
No. The view definition is defined by the SELECT statement only. The modular object intended mainly for data manipulation...
Hands-On Data Science with SQL Server 2017
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Hands-On Data Science with SQL Server 2017
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Overview of this book
SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features.
Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples.
By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs.
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Current Title:
Hands-On Data Science with SQL Server 2017
Table of Contents (14 chapters)
Preface
Free Chapter
Data Science Overview
SQL Server 2017 as a Data Science Platform
Data Sources for Analytics
Data Transforming and Cleaning with T-SQL
Data Exploration and Statistics with T-SQL
Custom Aggregations on SQL Server
Data Visualization
Data Transformations with Other Tools
Predictive Model Training and Evaluation
Making Predictions
Getting It All Together - A Real-World Example
Next Steps with Data Science and SQL
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