Insights from Data with BigQuery

Insights from Data with BigQuery

Google BDL-INSBIGQ

USD 59.40

Learn about the following basic features of BigQuery: write SQL queries, create and manage database tables in Cloud SQL, query public tables, and load sample data into BigQuery, troubleshoot common Syntax errors with the Query Validator, use Google Apps Script, create a chart in Google Sheets, and export that data to Google Slides, and create reports in Google Data Studio by connecting to BigQuery data.

USD 59.40

Introduction to SQL for BigQuery and Cloud SQL

Module 1

Introduction to SQL for BigQuery and Cloud SQL

Introduction to SQL for BigQuery and Cloud SQL

Google LIS-GOOGLE-2194
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

SQL (Structured Query Language) is a standard language for data operations that allows you to ask questions and get insights from structured datasets. It's commonly used in database management and allows you to perform tasks like transaction record writing into relational databases and petabyte-scale data analysis.

This lab serves as an introduction to SQL and is intended to prepare you for the many labs and quests in Qwiklabs on data science topics. This lab is divided into two parts: in the first half, you will learn fundamental SQL querying keywords, which you will run in the BigQuery console on a public dataset that contains information on London bikeshares.

In the second half, you will learn how to export subsets of the London bikeshare dataset into CSV files, which you will then upload to Cloud SQL. From there you will learn how to use Cloud SQL to create and manage databases and tables. Towards the end, you will get hands-on practice with additional SQL keywords that manipulate and edit data.

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BigQuery: Qwik Start - Console

Module 2

BigQuery: Qwik Start - Console

BigQuery: Qwik Start - Console

Google LIS-GOOGLE-2102
USD 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 an enterprise 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 in the Console, the classic Web UI or a command-line tool, or by making calls to the BigQuery REST API using a variety of client libraries such as Java, .NET, or Python. There are also a variety of third-party tools that you can use to interact with BigQuery, such as visualizing the data or loading the data.

This hands-on lab shows you how to use the Web UI to query public tables and load sample data into BigQuery.

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BigQuery: Qwik Start - Command Line

Module 3

BigQuery: Qwik Start - Command Line

BigQuery: Qwik Start - Command Line

Google LIS-GOOGLE-2020
USD 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.

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Exploring Your Ecommerce Dataset with SQL in Google BigQuery

Module 4

Exploring Your Ecommerce Dataset with SQL in Google BigQuery

Exploring Your Ecommerce Dataset with SQL in Google BigQuery

Google LIS-GOOGLE-2222
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

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.

We have a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into a table in BigQuery. In this lab, you use a copy of that dataset. Sample scenarios are provided, from which you look at the data and ways to remove duplicate information. The lab then steps you through further analysis the data.

To follow and experiment with the BigQuery queries provided to analyze the data, see Standard SQL Query Syntax.

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Troubleshooting Common SQL Errors with BigQuery

Module 5

Troubleshooting Common SQL Errors with BigQuery

Troubleshooting Common SQL Errors with BigQuery

Google LIS-GOOGLE-2226
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

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 you can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

A newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store has been loaded into BigQuery. You have a copy of that dataset for this lab and will explore the available fields and row for insights.

This lab steps you through the logic of troubleshooting queries. It provides activities within the context of a real-world scenario. Throughout the lab, imagine you're working with a new data analyst on your team, and they've provided you with their queries below to answer some questions on your ecommerce dataset. Use the answers to fix their queries to get a meaningful result.

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Explore and Create Reports with Data Studio

Module 6

Explore and Create Reports with Data Studio

Explore and Create Reports with Data Studio

Google LIS-GOOGLE-2221
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

Google Data Studio turns your data into informative dashboards and reports that are easy to read, easy to share, and fully customizable. Dashboarding allows you to tell great data stories to support better business decisions.

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.

The dataset used in this lab is an ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. You will explore the available fields and row of the dataset for insights.

This lab focuses on how to create new reports and explore your ecommerce dataset visually for insights.

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