Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth – a Case Study

7. Analytics Using Complex Data Types

Activity 9: Sales Search and Analysis


  1. First, create the materialized view on the customer_sales table:
    CREATE MATERIALIZED VIEW customer_search AS (
            customer_json -> 'customer_id' AS customer_id,
            to_tsvector('english', customer_json) AS search_vector
        FROM customer_sales
  2. Create the GIN index on the view:
    CREATE INDEX customer_search_gin_idx ON customer_search USING GIN(search_vector);
  3. We can solve the request by using our new searchable database:
    FROM customer_search 
    WHERE search_vector @@ plainto_tsquery('english', 'Danny Bat');

    This results in eight matching rows:

    Figure 7.29: Resulting...