Google Looker Masterclass: Looker & LookML A-Z 2026

In Lecture 9 of the Google Looker Masterclass, we will delve into the fundamental concepts of Dimensions and Measures within the context of Looker and LookML. We will discuss how dimensions represent the categorical data in a dataset, such as categories, labels, or groups, which can be used to slice and dice the data for analysis. Additionally, we will explore how measures represent numerical data that can be aggregated, such as sums, averages, or counts, providing insights into the performance and trends within the dataset.

Furthermore, we will cover the relationship between dimensions and measures in Looker, highlighting how dimensions are used to organize and filter the data while measures are used to calculate and summarize the data. We will also discuss best practices for creating and utilizing dimensions and measures within Looker, optimizing the analysis and visualization of data for business intelligence purposes. By the end of this lecture, students will have a solid understanding of how dimensions and measures contribute to the overall data modeling process in Looker and how to effectively leverage these concepts in their own analytical projects.

In today's lecture, we will be diving into the practical aspects of using Looker and LookML. We will start by creating our first Look, which is a data visualization tool that allows users to explore their data in a visually engaging way. We will go over the basics of Look creation, including selecting the data set to analyze, choosing the appropriate visualization type, and customizing the look and feel of the visualization.

Throughout this lecture, we will walk through the step-by-step process of creating a Look using LookML, Looker's modeling language. We will cover how to define dimensions and measures, create filters and conditions, and add custom calculations to enhance the analysis. By the end of this session, you will have a solid understanding of how to create your own Looks in Looker using LookML, setting you up for success in your data exploration and analysis journey.

In Lecture 11 of our Google Looker Masterclass, we will dive into the practical aspects of using Looker and LookML. This section will focus on how to save a Look, which is a specific visualization or report that you have created within Looker. We will walk through the steps of saving a Look, including how to name it, add a description, and specify privacy settings such as who can access it.

Additionally, we will explore advanced features such as scheduling Looks to be run at specific intervals, and setting up alerts for when certain conditions are met within your data. By the end of this lecture, you will have a thorough understanding of how to save and manage Looks in Looker, allowing you to effectively analyze and share insights with your team or organization.

In this lecture, we will delve into the topic of filtering using dimensions in Looker and LookML. We will explore how to use dimensions to narrow down your data and focus on specific subsets of information within your database. By applying filters using dimensions, you can easily extract the exact data you need for your analysis, making your reports more precise and actionable.

We will discuss the various ways you can filter your data using dimensions, including filtering by time, location, product, and other relevant attributes. We will also cover advanced filtering techniques and best practices to help you effectively query and manipulate your data using Looker and LookML. By the end of this lecture, you will have a solid understanding of how to leverage dimensions for filtering in your data analysis process.

In this lecture, we will delve into the topic of filtering using measures in Looker and LookML. We will explore how to filter data based on numerical values, such as revenue, quantity sold, or other metric-based measures. By utilizing measures in our filter conditions, we can narrow down our data set to focus on specific ranges or thresholds that are relevant to our analysis.

Moreover, we will discuss the different types of filters that can be applied to measures, including equal to, not equal to, less than, greater than, between, and more complex filter conditions. Understanding how to properly apply filters using measures is crucial in ensuring the accuracy and relevance of our data queries in Looker. By the end of this lecture, participants will have a solid grasp of how to effectively filter data using measures in Looker and LookML.

In Lecture 14 of the Google Looker Masterclass, we will be diving deep into the topic of Single-Value Visualization. We will explore different types of single-value visualizations, such as KPIs, gauges, and bullet charts, and learn how to effectively use them to represent key performance indicators in a visually appealing way. We will also discuss best practices for designing single-value visualizations and how to customize them using LookML to meet specific business needs.

Furthermore, we will cover how to create single-value visualizations in Looker using LookML by defining measures, dimensions, and filters. We will walk through examples of creating single-value visualizations for different use cases and demonstrate how to customize the appearance and interactivity of these visualizations. By the end of this lecture, you will have a comprehensive understanding of how to leverage single-value visualizations in Looker to effectively communicate data insights and drive informed decision-making within your organization.

In Lecture 15 of the Google Looker Masterclass, we will be diving into popular chart types, focusing specifically on bar and column charts. We will explore the differences between these two types of charts and when it is best to use each one. We will also discuss the various settings and configurations you can use to customize and enhance your bar and column charts for maximum impact and clarity.

Additionally, we will go over real-world examples of how bar and column charts can be used effectively to visualize data in a meaningful way. By the end of this lecture, you will have a solid understanding of how to create and customize bar and column charts using Looker and LookML, and be able to apply these skills to your own data visualization projects.

In Lecture 16 of Section 5: Popular Chart Types in the Google Looker Masterclass, we will delve into the intricacies of creating and customizing Line Charts using Looker and LookML. We will explore how Line Charts can effectively visualize trends over time or compare data points across different categories. We will discuss best practices for formatting Line Charts and how to ensure they communicate the desired insights to stakeholders effectively.

During this lecture, we will cover the step-by-step process of creating a Line Chart in Looker, including selecting the appropriate dimensions and measures, setting up filters, and applying advanced calculations using LookML. Additionally, we will discuss how to add custom annotations, labels, and legends to Line Charts to enhance their readability and make them more informative. By the end of this lecture, students will have a comprehensive understanding of how to leverage Line Charts in Looker to analyze data and present findings in a visually compelling way.

In this lecture, we will dive into the popular chart type known as the Pie Chart. We will explore how to create visually appealing pie charts using Looker and LookML. We will discuss when to use pie charts and how to effectively present data in a way that is easily understandable to viewers. Additionally, we will cover best practices for labeling and customizing pie charts to make them more informative and engaging for your audience.

Furthermore, we will demonstrate how to use Looker's built-in features to create highly customizable pie charts that can be tailored to suit your specific data visualization needs. By the end of this lecture, you will be equipped with the knowledge and skills needed to create stunning pie charts that effectively communicate your data insights. Join us as we uncover the power of pie charts in data visualization and how they can enhance your overall data analysis efforts.

In Lecture 18 of our Google Looker Masterclass, we will be covering the popular chart type of scatterplot. We will learn how to create effective scatterplots using Looker and LookML, and how to interpret and analyze the data presented in this type of visualization. Scatterplots are commonly used to show relationships between two variables, and we will explore different ways to customize and enhance scatterplots to better convey insights to our audience.

During this lecture, we will discuss the importance of choosing the right variables for a scatterplot and how to determine the best way to represent the data visually. We will also delve into using filters, color coding, and tooltips to make our scatterplots more interactive and engaging for viewers. By the end of this lecture, students will have a solid understanding of when and how to use scatterplots effectively in their data analysis and visualization projects.

In Lecture 19 of the Google Looker Masterclass, we will be diving into the world of GeoMaps. We will explore how to create interactive and visually appealing maps using Looker and LookML. We will cover the different types of GeoMaps available in Looker, including point maps, heat maps, and choropleth maps. We will also discuss how to customize and style your GeoMaps to suit your data visualization needs.

Additionally, we will walk through practical examples of how to use GeoMaps to analyze and understand geographical data sets. We will demonstrate how to plot location-based data on a map, how to visualize spatial patterns, and how to create impactful presentations using GeoMaps. By the end of this lecture, you will have the skills and knowledge needed to create informative and engaging GeoMaps for your data analysis projects.

In this lecture, we will dive into the topic of custom fields within Looker and LookML. Custom fields allow you to create calculated fields within your data, enabling you to perform more advanced analysis and reporting. We will discuss how to create custom fields using LookML, as well as explore different functions and operators that can be used to define custom calculations.

Furthermore, we will focus on table calculations in Looker, which are calculations that are performed on the data displayed in a table or visualization. We will cover how to create table calculations, apply them to various visualizations, and customize their behavior. By mastering custom fields and table calculations, you will be able to unlock the full potential of Looker and enhance your data analysis capabilities.

In Lecture 21 of Section 6 of the Google Looker Masterclass, we will be focusing on creating custom dimensions using Looker expressions. We will learn how to leverage LookML to define custom fields that are not readily available in our dataset. By the end of this lecture, students will be able to understand how to write Looker expressions to generate custom dimensions that meet their specific analytical needs.

Additionally, we will explore the different types of Looker expressions that can be used to create custom dimensions, such as arithmetic operations, string manipulations, and conditional statements. Through hands-on examples and practical exercises, students will have the opportunity to apply their knowledge of LookML to create and manipulate custom dimensions effectively. By the end of this lecture, participants will have a solid foundation in utilizing Looker expressions to enhance their data analysis capabilities.

In Lecture 22 of Section 6 of our Google Looker Masterclass, we will be diving into the concept of binning and how it can be used to create custom dimensions in Looker. Binning is the process of grouping numerical data into discrete bins or categories, allowing users to easily analyze and visualize the data in a more structured manner. We will learn how to use LookML to create binning logic that suits our specific data sets and business needs, and how to apply this logic to customize our dimension fields.

During this lecture, we will explore various examples of how binning can be applied to different types of data, such as age ranges, sales revenue bands, customer satisfaction scores, and more. We will also cover advanced techniques for binning, including defining custom bin sizes, setting up thresholds for each bin, and incorporating conditional logic to assign data points to the appropriate bins. By the end of this session, participants will have a solid understanding of how to leverage binning to create custom dimensions that enhance their data analysis capabilities within the Looker platform.

In Lecture 23 of Section 6: Custom fields in the Google Looker Masterclass, we will be diving into the topic of Groups - Custom Dimension. This lecture will explore how to create custom dimensions within Looker in order to group data in a more meaningful way. We will learn how to define custom fields and how to use them in LookML to generate insightful reports and visualizations.

Furthermore, we will discuss the different ways custom dimensions can be used in Looker to segment and analyze data. By the end of this lecture, students will have a solid understanding of how to create custom fields, define custom dimensions, and leverage them effectively in LookML. This knowledge will enable them to take their data analysis skills to the next level and extract valuable insights from their datasets using Looker.

In Lecture 24 of the Google Looker Masterclass, we will be delving into the topic of Custom Measures. This lecture will cover how to create custom measures within Looker using LookML. We will explore the different functions and syntax used to define custom measures, as well as how to manipulate and combine existing measures to create new, insightful metrics for analysis. By the end of this lecture, students will have a comprehensive understanding of how to leverage custom measures to extract valuable insights from their data using Looker.

Additionally, we will discuss best practices for organizing and documenting custom measures within LookML. Students will learn how to create clear and efficient code that is easily understandable and maintainable. We will also cover how to test and validate custom measures to ensure accuracy and reliability in data analysis. By the end of this lecture, students will have the knowledge and skills to effectively utilize custom measures in Looker to enhance their data analysis capabilities.

In Lecture 25 of our Google Looker Masterclass, we will delve into the Look view-mode in Looker. We will explore how to navigate and use this powerful feature to visualize and interact with data in a meaningful way. We will cover the different options available in the Look view-mode, including customizing visualizations, filtering data, and sharing insights with stakeholders.

Furthermore, we will discuss best practices for leveraging LookML in the Look view-mode to create more advanced and customized visualizations. By the end of this lecture, you will have a thorough understanding of how to effectively use the Look view-mode to analyze and communicate data-driven insights within your organization. Join us as we unlock the full potential of Looker and LookML in Section 7 of our course.

In Lecture 26 of Section 7 of the Google Looker Masterclass, we will be diving deeper into the various other options available in the Look View mode. We will explore features such as filtering options, drill-down capabilities, and customizations for visualizations. By the end of this lecture, you will have a comprehensive understanding of how to optimize your data analysis experience in Looker through the use of these advanced options.

Additionally, we will discuss the importance of utilizing LookML to enhance your data visualizations in Looker. We will cover topics such as creating custom fields, building dynamic dashboards, and incorporating advanced calculations into your reports. By leveraging these LookML capabilities, you will be able to take your data analysis skills to the next level and unlock new insights from your datasets.

In today's lecture, we will delve into the fascinating world of Looker Dashboards. We will learn how to create visually appealing and interactive dashboards using Looker's powerful data visualization tools. By the end of this session, you will be equipped with the knowledge and skills to design dynamic dashboards that provide valuable insights into your data.

We will explore the various components of a Looker Dashboard, including tiles, filters, and text elements. I will walk you through the process of designing and customizing dashboards using Looker's user-friendly interface. Additionally, I will demonstrate how to incorporate LookML (Looker Modeling Language) into your dashboard design to enable advanced customization and ensure seamless integration with your data sources. By the end of this lecture, you will have the tools and know-how to create compelling dashboards that effectively communicate your data-driven insights.

In this lecture, we will be diving into the topic of adding filters to a dashboard in Looker. We will explore how filters can enhance the user experience by allowing them to dynamically interact with the data displayed on the dashboard. We will learn how to create and customize filters to give users the flexibility to tailor the data to their specific needs and preferences. By the end of this lecture, you will have a comprehensive understanding of how filters work in Looker dashboards and be equipped with the skills to implement them effectively in your own projects.

Additionally, we will discuss best practices for using filters in a dashboard to ensure a seamless and intuitive user experience. We will cover common pitfalls to avoid when setting up filters, as well as tips and tricks for optimizing their functionality. By the end of this lecture, you will have the knowledge and expertise to create dynamic and interactive dashboards using filters in Looker, allowing you to provide valuable insights to your users in a visually appealing and user-friendly manner.

In this lecture, we will dive into setting up the tiles and other options in Looker Dashboards. We will explore how to configure the layout and appearance of the tiles on your dashboard to effectively communicate data insights to users. By adjusting the sizing, spacing, and placement of tiles, you can create a visually appealing and informative dashboard that meets the needs of your audience.

Additionally, we will cover how to customize the options available for each tile, such as filters, colors, and drill-down capabilities. Understanding how to set up these options will allow you to tailor the dashboard to the specific requirements of different stakeholders and enhance the interactive experience for users. By the end of this lecture, you will have the knowledge and skills to create dynamic and user-friendly dashboards using Looker and LookML.

Today, in Section 9 of our Google Looker Masterclass, we will be diving into the topic of folders in Looker. Folders are a fantastic tool for organizing your LookML files, dashboards, and other assets within Looker. We will discuss how to create folders, including best practices for naming and structuring them effectively. Additionally, we will cover how to manage access to these folders, ensuring that only the appropriate team members have permissions to view and edit the contents. By the end of this lecture, you will have a thorough understanding of how to utilize folders in Looker to streamline your workflow and keep your project well-organized.

In this lecture, we will walk you through the step-by-step process of creating folders in Looker and assigning access permissions to them. We will also discuss strategies for managing access to folders, including creating user groups and customizing roles and permissions. By the end of this lecture, you will be equipped with the knowledge and skills necessary to leverage folders effectively in Looker, enhancing collaboration and organization within your team. So, get ready to learn how to create folders and manage access in Looker like a pro!

In Lecture 31 of the Google Looker Masterclass: Looker & LookML A-Z 2024, we will be diving into the topic of downloading Looks and dashboards. We will explore the various formats in which you can download Looks and dashboards, such as PDF, CSV, Excel, and image formats. We will also discuss best practices for downloading and sharing content to ensure that your data remains secure and easily accessible to stakeholders.

Additionally, we will cover how to schedule and automate the downloading and sharing of Looks and dashboards, using Looker's scheduling functionality. This will enable you to set up recurring reports and ensure that stakeholders receive up-to-date information without manual intervention. By the end of this lecture, you will have a thorough understanding of how to download and share content in Looker, enhancing your ability to communicate insights effectively within your organization.

In Lecture 32 of Section 10 of the Google Looker Masterclass, we will be covering the topic of sharing and sending emails. We will discuss how to easily share dashboards, reports, and visualizations with colleagues and clients using Looker. We will also explore the various options available for sending emails directly from the Looker platform, including scheduling automated reports to be sent at regular intervals.

Additionally, we will delve into the importance of securing sensitive information when sharing content via email. We will go over best practices for encrypting emails, setting access controls, and ensuring data privacy. By the end of this lecture, students will have a comprehensive understanding of how to effectively share and send emails using Looker, while also prioritizing data security and confidentiality.