This course is designed for data analysts who want to learn about using BigQuery for their data analysis needs. Through a combination of videos, labs, and demos, we cover various topics that discuss how to ingest, transform, and query your data in BigQuery to derive insights that can help in business decision making.
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Note: This course refreshes many basic topics covered in the From Data to Insights specialization about using BigQuery as a data analyst, and also covers new products like Dataform.
What you'll learn
- Learn the purpose and value of BigQuery, Google Cloud’s enterprise data warehouse, and discuss its data analytics features.
- Analyze, Clean and transform your data in BigQuery with SQL.
- Ingest new BigQuery datasets, and use Connected Sheets and Looker Studio to visualize data insights from BigQuery.
- Use Dataform to develop scalable data transformation pipelines in BigQuery.
Syllabus
Course Introduction
This module introduces the course agenda.
BigQuery for Data Analysts
In the first module, we look at analytics challenges faced by data analysts and compare big data on-premises versus on the Cloud. We then introduce BigQuery, which is Google Cloud’s enterprise data warehouse, and review its features that make BigQuery a great option for your data analytics needs. And finally, we’ll learn from real-world use cases of companies transformed through analytics on the Cloud.
Exploring and Preparing your Data with BigQuery
This module is all about exploring your data with SQL, or structured query language. We go from very simple select statements to more complex queries that explore various datasets.
Cleaning and Transforming your Data
In this module, we discuss principles about data integrity, and then we look at how to use SQL to clean, prepare, and transform your data. The last section of this module also briefly introduces other products like Dataprep, Cloud Data Fusion, Dataflow, Dataproc, and Dataform that can help with data preparation and transformation.
Ingesting and Storing New BigQuery Datasets
This module talks about ingesting and storing data into BigQuery native storage. We discuss when to use Extract and Load, versus Extract, Load and Transform, versus Extract Transform and Load approaches for loading data into BigQuery.We also cover external data sources, where you can run your query in BigQuery, but the data is hosted outside of BigQuery.
Visualizing Your Insights from BigQuery
This module is where all that hard work around ingesting, cleaning, preparing, and transforming your data comes to fruition as you get to visualize insights from your data by building insightful dashboards and reports. We start off with a little visualization theory and some best practices, and then look at tools, like Looker Studio and Connected Sheets, that can connect to BigQuery and help create impactful visualizations to capture and convey your insights. Although SQL is a powerful query language, programming languages such as Python, Java, or R provide syntaxes and a large array of built-in statistical functions that data analysts might find more expressive and easier to manipulate for certain types of data analysis. Such tools include open source web-based applications like Jupyter Notebooks, and so we discuss these as well.
Developing scalable data transformations pipelines in BigQuery with Dataform
Creating, maintaining, and versioning SQL pipelines is a lot of hard work. And many times, data analysts have to use multiple tools to achieve this. So in this module, we introduce Dataform, a new product that offers a unified end-to-end experience to develop, version control, and orchestrate SQL pipelines in BigQuery.
Summary
This module recaps the key topics covered in the course.
