In this chapter, you will learn about the unique features of BigQuery, how it runs large analytical queries, and how it compares to other data warehouses and databases.

Introduction to BigQuery

Starting with e-commerce

Unique attributes of BigQuery

BigQuery compared to a traditional SQL database

BigQuery Architecture

Classifying BigQuery components

Ordering the BigQuery architecture

BigQuery data organization

Table structure

BigQuery data rules

Tables in the wild

BigQuery Architecture and Structure

This chapter will cover the fundamentals of how to write queries in BigQuery while also providing a review of SQL concepts. You will also learn how to ingest bulk data into BigQuery and the different data types within BigQuery.

Writing queries in BigQuery

Exploring the course data with SQL

Finding order total value

Using joins

Data ingestion in BigQuery

Loading local data to BigQuery

Acceptable data formats

Structuring a LOAD DATA statement

Date/time types in BigQuery

Formatting dates for reports

Formatting timestamps and dates

Finding data within certain ranges

Using CURRENT_TIMESTAMP

Unstructured data

Create an array in a query

Use SEARCH with unstructured data

UNNEST-ing Data

Writing queries and data types

Learn how to query data using common table expressions, aggregations, and window functions.

Common table expressions

Filtering data with CTEs

Optimizing using CTEs

Using multiple CTEs

Aggregations

Classifying aggregate functions

Using COUNTIF

Filtering with HAVING

ANY_VALUE

Special aggregations in BigQuery

Logical aggregates

Using STRING_AGG and ARRAY_CONCAT_AGG

Approximate statistical functions

WINDOW Functions

Writing a window query

RANK and LEAD/LAG

Using row based WINDOW functions

Filtering with QUALIFY

 Querying data in BigQuery

Understand all join types in BigQuery, mange data with data manipulation, and optimize your queries.

Joins in BigQuery

Finding the correct join type

RIGHT, LEFT, and OUTER joins

UNNEST with CROSS JOINs

Joins with aggregations

Data manipulation language (DML) statements

Data manipulation types

Options

None

WHERE and JOIN

Any type

Using conditions in DML statements

True

False

Considerations using DML statements

Query optimization strategies

Limiting the amount of data

Finding the last 90 days of orders

Perform optimizations in BigQuery

Congratulations!

Data management and optimization

Embark on this course for practical insights into data analysis. Explore BigQuery's architecture, run analytical queries, and optimize workflows. Gain confidence in navigating datasets, query elements, and data manipulation. Unlock advanced techniques for efficient analysis and master the art of joining data. Upon completion, navigate BigQuery effectively, optimize queries, and analyze data swiftly.

Delve into the core concepts of BigQuery architecture, understanding its historical significance and comparing it to traditional database systems. Uncover the diverse range of applications where BigQuery shines, setting the stage for a deeper exploration of its capabilities.

<h2>Mastering Analytical Queries and Data Manipulation</h2>Learn to wield the power of BigQuery by executing analytical queries and manipulating data with precision. Navigate datasets and tables effortlessly while gaining proficiency in utilizing bq command line tools for data ingestion. Dive into the complexities of data types and unstructured data, honing your skills to extract meaningful insights.

<h2>Optimizing Workflow Efficiency and Query Performance</h2>Elevate your data analysis workflow to new heights by mastering advanced techniques within BigQuery. From optimizing query performance through strategic optimization strategies to mastering the intricacies of joining data, this section equips you with the tools needed to streamline your analytical processes. Unlock the potential of Data Manipulation Language (DML) statements, empowering you to perform complex data transformations with ease.

Data Manipulation in SQL

Unlock BigQuery's power: grasp its fundamentals, execute precise queries, and optimize workflows for efficient data analysis.

Unlock BigQuery's power: grasp its fundamentals, execute queries, and optimize workflows for efficient data analysis.

Using table partitions and clusters will help improve DML performance.

You do not need to use a WHERE condition with an UPDATE statement.

You should always run DML statements individually.

Considerations using DML statements

Introduction to BigQuery

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