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.

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.

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.

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.

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.

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.

In this module, we will start off by talking about what BigQuery Studio is, and the reason we built it. Next, we describe in a little more detail all the great capabilities that come with BigQuery Studio. In the end, we wrap up the module with a demo to walk you through the cool features and show you how to use it.

This module recaps the key topics covered in the course.