BigQuery for Data Warehousing

BigQuery for Data Warehousing

Google BDL-BQDW

GBP 59.40

Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

Write Your Own Review

Only registered users can write reviews. Please Sign in or create an account

GBP 59.40

BigQuery: Qwik Start - Command Line

Module 1

BigQuery: Qwik Start - Command Line

BigQuery: Qwik Start - Command Line

Google LIS-GOOGLE-2020
GBP 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

Storing and querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. BigQuery is a serverless, highly scalable cloud data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. Simply move your data into BigQuery and let us handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

You can access BigQuery by using the Console, Web UI or a command-line tool using a variety of client libraries such as Java, .NET, or Python. There are also a variety of solution providers that you can use to interact with BigQuery.

This hands-on lab shows you how to use bq, the python-based command line tool for BigQuery, to query public tables and load sample data into BigQuery.

See more See less
Creating a Data Warehouse Through Joins and Unions

Module 2

Creating a Data Warehouse Through Joins and Unions

Creating a Data Warehouse Through Joins and Unions

Google LIS-GOOGLE-2230
GBP 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.
See more See less
Creating Date-Partitioned Tables in BigQuery

Module 3

Creating Date-Partitioned Tables in BigQuery

Creating Date-Partitioned Tables in BigQuery

Google LIS-GOOGLE-2233
GBP 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.
See more See less
Troubleshooting and Solving Data Join Pitfalls

Module 4

Troubleshooting and Solving Data Join Pitfalls

Troubleshooting and Solving Data Join Pitfalls

Google LIS-GOOGLE-2223
GBP 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.
See more See less
Working with JSON, Arrays, and Structs in BigQuery

Module 5

Working with JSON, Arrays, and Structs in BigQuery

Working with JSON, Arrays, and Structs in BigQuery

Google LIS-GOOGLE-2238
GBP 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
In this lab you will work with semi-structured data (ingesting JSON, Array data types) inside of BigQuery. You will practice loading, querying, troubleshooting, and unnesting various semi-structured datasets.
See more See less
Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors

Module 6

Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors

Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors

Google LIS-GOOGLE-2356
GBP 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
In this lab you will explore existing datasets with Data Catalog and mine the table and column metadata for insights.
See more See less

* Required Fields