Data Analytics & Engineering
The data stack, end to end. BigQuery, Looker, Dataflow and the pipelines in between — warehousing, modelling and the engineering practice that keeps them reliable.
16 courses
AlloyDB Essentials
This course explores the benefits of AlloyDB, especially compared to PostgreSQL on Cloud SQL. It will walk you through AlloyDB’s unique architecture, and explain how to configure deployments on Google Cloud.The course is divided into two parts: AlloyDB Administration Essentials (architecture and configuration) and AlloyDB Optimization Essentials (performance tuning).
Analyzing and Visualizing Data in Looker
In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. You learn to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance. You also discover how to ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision-making.
BigQuery for Data Analysts
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.
Data Engineering on Google Cloud
Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.
Data Engineering Solutions Lab (DSL)
The Data Engineering Solutions Lab (DSL) is a 10-Day, focused, immersive learning experience that rapidly upskills your team on Google Cloud data engineering principles and best practices. The program combines five days of expert-led training sessions on key data engineering concepts (e.g., data warehousing, pipelines, data quality) with hands-on labs and a five-day collaborative capstone project. The capstone challenges participants to address a real-world use case using Google Cloud tools.
Data Integration with Cloud Data Fusion
This 2-day course introduces learners to Google Cloud’s data integration capability using Cloud Data Fusion. In this course, we discuss challenges with data integration and the need for a data integration platform (middleware). We then discuss how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats and generate insights. We take a look at Cloud Data Fusion’s main components and how they work, how to process batch data and real time streaming data with visual pipeline design, rich tracking of metadata and data lineage, and how to deploy data pipelines on various execution engines.
Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration
In this course, you learn about the internals of BigQuery and best practices for designing, optimizing, and administering your data warehouse. Through a combination of lectures, demos, and labs, you learn about BigQuery architecture and how to design optimal storage and schemas for data ingestion and changes.Next, you learn techniques to improve read performance, optimize queries, manage workloads, and use logging and monitoring tools. You also learn about the different pricing models. Finally, you learn various methods to secure data, automate workloads, and build machine learning models with BigQuery ML.
Developing Data Models with LookML
This course empowers you to develop scalable, performant Looker ModelingLanguage (LookML) models that provide your business users with the standardized,ready-to-use data that they need to answer their questions. Upon completing thiscourse, you will be able to start building and maintaining LookML models to curateand manage data in your organization’s Looker instance.
Enterprise Database Migration
This course is intended to give architects, engineers, and developers the skills requiredto help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to GCP while taking advantage of various GCP services. This course covers how to move on-premises, enterprise databases like SQL Serverto Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloudbare metal.
Introduction to Data Analytics on Google Cloud
This course is an introduction to data analytics on Google Cloud. It is designed for learners who have no prior experience with data analytics or Google Cloud. The course covers the basics of data analysis, including collection, storage, exploration, visualization, and sharing. It also introduces learners to Google Cloud's data analytics tools and services. Through video lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactful visualizations and dashboards.
Introduction to Data Engineering on Google Cloud
In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
Looker Developer Deep Dive
LookML serves as the foundation for visualization assets in Looker and is capable of dynamic aggregations, incrementally refreshed persistent derived tables, and more. In this course, you will practice the skills to be an advanced Looker Developer through guided lectures and independent exercises using sample data.
Orchestrate BigQuery Workloads with Dataform
Dataform is a service for data analysts to develop, test, version control, and schedule complex SQL workflows for data transformation in BigQuery. In this course you will explore the components of Dataform core, learn how to define tables and dependencies in SQLX, document BigQuery tables and views, understand BigQuery security settings and how to manage these with Dataform, write assertions, execute SQL workflows, and explore additional advanced use cases.
Serverless Data Processing with Dataflow
This training is intended for big data practitioners who want to further their understanding of Dataflow in order to advance their data processing applications. Beginning with foundations, this training explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in. The section on developing pipelines covers how you convert your business logic into data processing applications that can run on Dataflow. This training culminates with a focus on operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability.
Understanding Cloud Spanner
In this course, you learn about Cloud Spanner. You will get an introduction to Cloud Spanner and understand how it differs from other database products. You also learn when and how to use Cloud Spanner to solve your relational database needs at scale.
Workflow Orchestration with Cloud Composer
Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. Composer enables you to create, schedule, monitor, and manage workflow pipelines that span across clouds and on-premises data centers. In this course, you will learn about Apache Airflow and its implementation via Cloud Composer. You will learn how to provision Composer instances, create and manage Airflow DAGs on Composer, and perform tasks such as testing, debugging, and monitoring of Airflow DAGs.
Other categories
Not sure which course fits?
Book a 30-minute call. We'll look at where your team is, what they need to be able to do, and put together either a specific course date or a tailored learning plan.