This is a Google Cloud Self-Paced Lab. In this lab, you create SQL derived and native derived tables in LookML to define new tables that do not already exist in the underlying database.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will explore millions of New York City yellow taxi cab trips available in a BigQuery Public Dataset, create an ML model inside of BigQuery to predict the fare, and evaluate the performance of your model to make predictions.

In this project, you will install and explore five free Integrated Development Environments or IDEs on a Linux System. Linux is a popular operating system that is based on the Unix operating system. It is a popular Operating System for running efficient Application Servers, but also has a great Desktop available for running Integrated Development Environments for Application development. IDE’s offer the developer a single place to write, edit, debug, and launch programs in various languages. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this course you will be introduced to the process of designing a database. The old saying about a picture being worth a thousand words rings true in the database design process. Database designers document their designs using diagrams. To document your basic design, you will use a diagramming tool called “Dia”. You will review user requirements to identify the categories of data that will need to be included in the database, and then fill out those categories with details. You will also determine how the categories are logically related. Using Dia, you will document your logical database design using a standard database design diagram called an Entity Relationship Diagram. Generating the ERD is an important step in the database design process. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this course, we will cover the core concepts and practices of building and running Cloud Native applications and how to run these applications in a multicloud environment. We will cover technologies and practices including; microservices, DevOps, CI/CD, Docker, Kubernetes, and OpenShift. This course is designed for anyone wanting to learn about the guiding principles of building cloud native applications and managing them across multiple cloud platforms, both private and public. Also covered in this course is how to automate many of the common functions of building and running cloud native applications and orchestrating the environment they run in. A basic familiarity with cloud concepts and modern development practices is recommended. For the hands-on labs, an IBM Cloud account will be required as well as basic familiarity with command-line interfaces. This course is designed for anyone wanting to learn about the guiding principles of building cloud native applications and managing them across multiple cloud platforms, both private and public. Also covered in this course is how to automate many of the common functions of building and running cloud native applications and orchestrating the environment they run in.

This is a self-paced lab that takes place in the Google Cloud console. This lab shows you how to query public tables and load sample data into BigQuery using the Web UI. Watch the short videos Get Meaningful Insights with Google BigQuery and BigQuery: Qwik Start - Qwiklabs Preview.

In this course you will learn to write the SQL code to manipulate the data in a relational database table. You’ll begin by populating the table with data. Since a database and its tables are designed and built to be repositories of data, getting the data into the tables is a critical activity in the building of a working database. When building a new home, the real test of your design comes when the furniture and family move in. It’s much the same with designing and building database tables—the real test comes when you load data into the tables and begin to use it. As you work through and complete hands-on tasks, you’ll become familiar with SQLiteStudio, the database management system used in the course. You’ll experience first-hand the impact data types and constraints have on manipulating table data. For example, as you enter new data into a table, you’ll appreciate the extra protection provided by the primary key constraint. It will not let you insert two rows into a table that are exactly alike. In addition to adding data to the tables, you’ll write the SQL code used to modify existing data values and to delete rows of data. Managing and manipulating data are SQL’s primary purposes, and SQL coding will be a powerful addition to your tool set. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will examine how Dataprep can be used on complicated data structures in BigQuery.

Welcome to this project-based course SQL Window Functions for Analytics. This is a hands-on project that will help SQL users use window functions extensively for database insights. In this project, you will learn how to explore and query the project-db database extensively. We will start this hands-on project by retrieving the data in the table in the database. By the end of this 2-hour-and-a-half-long project, you will be able to use different window functions to retrieve the desired result from a database. In this project, you will learn how to use SQL window functions like ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), and LAST_VALUE() to manipulate data in the project-db database. Also, we will consider how to use aggregate window functions. These window functions will be used together with the OVER() clause to query this database. By extension, we will use grouping functions like GROUPING SETS(), ROLLUP(), and CUBE() to retrieve sublevel and grand totals.