It takes the average reader 11 hours and 28 minutes to read Querying Databricks with Spark SQL by Adam Aspin
Assuming a reading speed of 250 words per minute. Learn more
A practical guide to using Spark SQL to perform complex queries on your Databricks data KEY FEATURES ● Learn SQL from the ground up, with no prior programming or SQL knowledge required. ● Progressively build your knowledge and skills, from basic data querying to complex analytics. ● Gain hands-on experience with SQL, covering all levels of knowledge from novice to expert. DESCRIPTION Databricks stands out as a widely embraced platform dedicated to the creation of data lakes. Within its framework, it extends support to a specialized version of Structured Query Language (SQL) known as Spark SQL. If you are interested in learning more about how to use Spark SQL to analyze data in a data lake, then this book is for you. The book covers everything from basic queries to complex data-processing tasks. It begins with an introduction to SQL and Spark. It then covers the basics of SQL, including data types, operators, and clauses. The next few chapters focus on filtering, aggregation, and calculation. Additionally, it covers dates and times, formatting output, and using logic in your queries. It also covers joining tables, subqueries, derived tables, and common table expressions. Additionally, it discusses correlated subqueries, joining and filtering datasets, using SQL in calculations, segmenting and classifying data, rolling analysis, and analyzing data over time. The book concludes with a chapter on advanced data presentation. By the end of the book, you will be able to use Spark SQL to perform complex data analysis tasks on data lakes. WHAT YOU WILL LEARN ● Use Spark SQL to read data from a data lake. ● Learn how to filter, aggregate, and calculate data using Spark SQL. ● Learn how to join tables, use subqueries, and create derived tables in Spark SQL. ● Analyze data over time using Spark SQL to track trends and identify patterns in data. ● Present data in a visually appealing way using Spark SQL. WHO THIS BOOK IS FOR This book is for anyone who wants to learn how to use SQL to analyze big data. Whether you are a data analyst, student, database developer, accountant, business analyst, data scientist, or anyone else who needs to extract insights from large datasets, this book will teach you the skills you need to get the job done. TABLE OF CONTENTS 1. Writing Basic SQL Queries 2. Filtering Data 3. Applying Complex Filters to Queries 4. Simple Calculations 5. Aggregating Output 6. Working with Dates in Databricks 7. Formatting Text in Query Output 8. Formatting Numbers and Dates 9. Using Basic Logic to Enhance Analysis 10. Using Multiple Tables When Querying Data 11. Using Advanced Table Joins 12. Subqueries 13. Derived Tables 14. Common Table Expressions 15. Correlated Subqueries 16. Datasets Manipulation 17. Using SQL for More Advanced Calculations 18. Segmenting and Classifying Data 19. Rolling Analysis 20. Analyzing Data Over Time 21. Complex Data Output
Querying Databricks with Spark SQL by Adam Aspin is 675 pages long, and a total of 172,125 words.
This makes it 228% the length of the average book. It also has 210% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 15 hours and 40 minutes to read Querying Databricks with Spark SQL aloud.
Querying Databricks with Spark SQL is suitable for students ages 12 and up.
Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.
When deciding what to show young students always use your best judgement and consult a professional.
Querying Databricks with Spark SQL by Adam Aspin is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.
To buy Querying Databricks with Spark SQL by Adam Aspin on Amazon click the button below.
Buy Querying Databricks with Spark SQL on Amazon