Architecting Data Warehousing Solutions Using Google BigQuery from Pluralsight

BigQuery is the Google Cloud Platform’s data warehouse on the cloud. In this course, you’ll learn how you can work with BigQuery on huge datasets with little to no administrative overhead.

Organizations store massive amounts of data that gets collated from a wide variety of sources. BigQuery supports fast querying at a petabyte scale, with serverless functionality and autoscaling. BigQuery also supports streaming data, works with visualization tools, and interacts seamlessly with Python scripts running from Datalab notebooks.

In this course, Architecting Data Warehousing Solutions Using Google BigQuery, you’ll learn how you can work with BigQuery on huge datasets with little to no administrative overhead related to cluster and node provisioning.

First, you'll start off with an overview of the suite of storage products on the Google Cloud and the unique position that BigQuery holds. You’ll see how BigQuery compares with Cloud SQL, BigTable, and Datastore on the GCP and how it differs from Amazon Redshift, the data warehouse on AWS.

Next, you’ll create datasets in BigQuery which are the equivalent of databases in RDMBSes and create tables within datasets where actual data is stored. You’ll work with BigQuery using the web console as well as the command line. You’ll load data into BigQuery tables using the CSV, JSON, and AVRO format and see how you can execute and manage jobs.

Finally, you'll wrap up by exploring advanced analytical queries which use nested and repeated fields. You’ll run aggregate operations on your data and use advanced windowing functions as well. You’ll programmatically access BigQuery using client libraries in Python and visualize your data using Data Studio.

At the end of this course, you'll be comfortable working with huge datasets stored in BigQuery, executing analytical queries, performing analysis, and building charts and graphs for your reports.

What's inside

Syllabus

Course Overview

Understanding BigQuery in the GCP Service Taxonomy

Using Datasets, Tables, and Views in BigQuery

Getting Data in and out of BigQuery

Performing Advanced Analytical Queries in BigQuery

Programmatically Accessing BigQuery from Client Programs

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Appropriate for learners interested in Data Warehousing and Data Science

Stresses the seamless interaction with Python scripts running from Datalab notebooks

Introduces the Google Cloud Platform’s suite of storage products

Develops the skill of performing advanced analytical queries

Instructs on managing and executing jobs within BigQuery

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Architecting Data Warehousing Solutions Using Google BigQuery with these activities:

Compile and Organize Course Materials

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Enhance learning by organizing and reviewing course materials systematically, including notes, assignments, and practice questions.

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Collect and organize course materials
Review materials regularly
Identify areas for further study

Review SQL Basics for BigQuery

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Ensure a strong foundation by reviewing core SQL concepts and syntax to prepare for working with BigQuery effectively.

Browse courses on SQL

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Review online tutorials or documentation
Practice writing basic SQL queries

Attend BigQuery Community Meetups and Events

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Connect with other professionals and learn about best practices and the latest trends in BigQuery by attending industry events and meetups.

Browse courses on BigQuery

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Identify local BigQuery meetups or webinars
Attend the event and participate in discussions

Four other activities

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Identify and Reach Out to BigQuery Experts

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Gain valuable guidance by seeking out and connecting with experienced BigQuery professionals who can provide personalized advice and support.

Browse courses on BigQuery

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Attend BigQuery events or join online communities
Identify potential mentors
Reach out and introduce yourself
Request guidance and support on specific topics

Guided Tutorial: Building a Data Warehouse in Google Cloud

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Reinforce and refine the concepts by following a step-by-step guided tutorial to build a fully functional data warehouse in Google Cloud using BigQuery.

Browse courses on Data Warehousing

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Review the Google Cloud Data Warehouse tutorial
Create a Google Cloud Platform account
Create a BigQuery dataset and table
Load data into BigQuery
Run queries on your data

SQL Practice Drills: Data Aggregation and Windowing Functions

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Master advanced analytical queries in BigQuery by practicing SQL data aggregation and windowing functions through a series of exercises.

Browse courses on SQL

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Review SQL aggregation and windowing functions
Install a SQL practice tool or use an online editor
Solve problems involving data aggregation and windowing

Project: Design and Build a Data Pipeline in BigQuery

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Apply BigQuery concepts by designing and building a complete data pipeline, from data ingestion to analysis and reporting.

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Define the data pipeline requirements
Design the data flow and architecture
Implement the data pipeline in BigQuery
Test and validate the data pipeline
Document and present the data pipeline

Career center

Learners who complete Architecting Data Warehousing Solutions Using Google BigQuery will develop knowledge and skills that may be useful to these careers:

BI Analyst

A BI Analyst uses data to create reports and dashboards that can be used to track progress and make informed decisions. This course, Architecting Data Warehousing Solutions Using Google BigQuery, can help build a foundation for success as a BI Analyst by teaching the basics of getting, working with, and querying large datasets stored in the Google Cloud Platform's data warehouse on the cloud, BigQuery. These skills can be beneficial for a BI Analyst who needs to access and analyze large datasets.

See salaries and explore the career path for BI Analyst

Data Analyst

A Data Analyst collects, processes, analyzes, and interprets data on behalf of a company for the purpose of identifying trends, patterns, and other useful information. This information can then be used to make informed decisions about products, marketing strategies, and other business initiatives. This course, Architecting Data Warehousing Solutions Using Google BigQuery, can help build a foundation for success as a Data Analyst by teaching the basics of getting, working with, and querying large datasets stored in the Google Cloud Platform's data warehouse on the cloud, BigQuery.

See salaries and explore the career path for Data Analyst

Big Data Engineer

A Big Data Engineer works with large datasets to develop and implement data solutions. This course, Architecting Data Warehousing Solutions Using Google BigQuery, can help build a foundation for success as a Big Data Engineer by teaching the basics of getting, working with, and querying large datasets stored in the Google Cloud Platform's data warehouse on the cloud, BigQuery. These skills can be beneficial for a Big Data Engineer who needs to access and analyze large datasets.

See salaries and explore the career path for Big Data Engineer

Data Scientist

A Data Scientist uses data to build models that can be used to predict future outcomes or trends. This course, Architecting Data Warehousing Solutions Using Google BigQuery, can help build a foundation for success as a Data Scientist by teaching the basics of getting, working with, and querying large datasets stored in the Google Cloud Platform's data warehouse on the cloud, BigQuery. These skills can be beneficial for a Data Scientist who needs to access and analyze large datasets.

See salaries and explore the career path for Data Scientist

Business Analyst

A Business Analyst uses data to identify and solve business problems. This course, Architecting Data Warehousing Solutions Using Google BigQuery, can help build a foundation for success as a Business Analyst by teaching the basics of getting, working with, and querying large datasets stored in the Google Cloud Platform's data warehouse on the cloud, BigQuery. These skills can be beneficial for a Business Analyst who needs to access and analyze large datasets.

See salaries and explore the career path for Business Analyst

Data Architect

A Data Architect designs and develops the architecture for data management systems. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as understanding the unique position that BigQuery holds in the Google Cloud service taxonomy and how it compares to other data warehouse services. These skills can be beneficial for a Data Architect who is responsible for designing and developing data warehouse architectures.

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Product Manager

A Product Manager plans and manages the development and release of a product. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be helpful as it covers topics such as understanding the unique position that BigQuery holds in the Google Cloud service taxonomy and how it compares to other data warehouse services. These skills can be beneficial for a Product Manager who needs to make decisions about which data warehouse service to use.

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Consultant

A Consultant provides advice to businesses on how to improve their operations. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be helpful as it covers topics such as understanding the unique position that BigQuery holds in the Google Cloud service taxonomy and how it compares to other data warehouse services. These skills can be beneficial for a Consultant who needs to advise clients on data warehousing solutions.

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Solutions Architect

A Solutions Architect designs and implements solutions to meet the needs of a business. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be helpful as it covers topics such as understanding the unique position that BigQuery holds in the Google Cloud service taxonomy and how it compares to other data warehouse services. These skills can be beneficial for a Solutions Architect who needs to design and implement data warehousing solutions.

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Data Engineer

A Data Engineer builds and maintains the systems and infrastructure that are used to store, process, and analyze data. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as creating datasets and tables within BigQuery, loading data into and out of BigQuery tables, and executing and managing jobs. These skills can be beneficial for a Data Engineer who is responsible for managing BigQuery.

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IT Manager

An IT Manager is responsible for the overall management of an organization's IT systems and infrastructure. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as understanding the unique position that BigQuery holds in the Google Cloud service taxonomy and how it compares to other data warehouse services. These skills can be beneficial for an IT Manager who is responsible for making decisions about data warehousing solutions.

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Database Developer

A Database Developer designs, develops, and maintains databases. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as creating datasets and tables within BigQuery, loading data into and out of BigQuery tables, and executing and managing jobs. These skills can be beneficial for a Database Developer who is responsible for developing and maintaining BigQuery databases.

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Database Administrator

A Database Administrator (DBA) designs, creates, and maintains the databases that are used to store and manage data. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as creating datasets and tables within BigQuery, loading data into and out of BigQuery tables, and executing and managing jobs. These skills can be beneficial for a DBA who is responsible for managing BigQuery.

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Data Warehouse Manager

A Data Warehouse Manager is responsible for the overall management of a data warehouse. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as understanding the unique position that BigQuery holds in the Google Cloud service taxonomy and how it compares to other data warehouse services. These skills can be beneficial for a Data Warehouse Manager who is responsible for managing a BigQuery data warehouse.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course, Architecting Data Warehousing Solutions Using Google BigQuery, may be of interest as it covers topics such as programmatically accessing BigQuery using client libraries in Python. These skills can be beneficial for a Software Engineer who is responsible for developing applications that use BigQuery.

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