Data Analyst Path
Data Engineer Path
Database Engineer Path
Google Cloud Big Data and Machine Learning Fundamentals
1 DAY
This course will introduce you to Google Cloud’s big data and machine learning functions. You’ll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.
From Data to Insights with Google Cloud Platform
3 DAYS
Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course!
This three-day instructor-led class teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.
Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration
3 DAYS
This three-day course is a detailed analysis of BigQuery. Through a combination of lectures, demos, and hands-on labs, you get a closer look at BigQuery internals and use this knowledge to optimize performance. You will start Day 1 with a detailed look at BigQuery architecture and learn how to design your storage and schema for optimization and handle data ingestion and changes. On Day 2, you learn techniques to improve read performance and optimize your queries, manage workload, use logging and monitoring tools, and familiarize yourself with pricing models. Day 3 covers various methods to secure your data, automate your workloads, and build machine learning models in BigQuery.
Analyzing and Visualizing Data in Looker
1 DAY
In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done only by SQL-savvy developers or analysts. Upon completion of this course, you will be able to leverage Looker’s modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
Developing Data Models with LookML
1 DAY
This course empowers you to develop scalable, performant LookML (Looker
Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
Looker Developer Deep Dive
2 DAYS
LookML not only serves as the foundation for visualization assets in Looker, but is also 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 lecture and independent exercises using sample data.
Migrating Amazon Athena Users to BigQuery and Dataproc
1 DAY
In this course, you will learn how to translate various concepts in Amazon Athena to the analogous concepts in BigQuery and Dataproc. You will learn how the high-level storage and compute architectures of Amazon Athena compare to BigQuery and Dataproc, understand how to configure datasets and tables in BigQuery, understand schema mappings from Amazon Athena to BigQuery and schema optimization in BigQuery. You will also learn how to create ephemeral Dataproc clusters for Spark data processing jobs and best practices around resource management for these jobs.
Migrating Amazon Redshift Users to BigQuery
1 DAY
In this course, you will learn how to translate various concepts in Amazon Redshift to the analogous concepts in BigQuery. You will learn how the high-level architectures of Amazon Redshift and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Amazon Redshift to data types in BigQuery, understand schema mapping from Amazon Redshift to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Amazon Redshift and BigQuery.
Migrating Snowflake Users to BigQuery
1 DAY
In this course, you will learn how to translate various concepts in Snowflake to the analogous concepts in BigQuery. You will learn how the high-level architectures of Snowflake and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Snowflake to data types in BigQuery, understand schema mapping from Snowflake to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Snowflake and BigQuery.
Migrating Teradata Users to BigQuery
1 DAY
In this course, you will learn how to translate various concepts in Teradata to the analogous concepts in BigQuery. You will learn how the high-level architectures of Teradata and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Teradata to data types in BigQuery, understand schema mapping from Teradata to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Teradata and BigQuery.
Google Cloud Big Data and Machine Learning Fundamentals
1 DAY
This course will introduce you to Google Cloud’s big data and machine learning functions. You’ll begin with a quick overview of Google Cloud and then dive deeper into its data processing capabilities.
Data Engineering on Google Cloud Platform
4 DAYS
This course introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other serviorks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring.This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data, and carry out machine learning. The course covers structured, unstructured, and streaming data.
Serverless Data Processing with Dataflow
3 DAYS
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.
Professional Data Engineer - Google Cloud Advanced Skills & Certification Workshop
2 DAYS
The workshop is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam.
In this workshop, we review the exam guidelines and product strategies for the major Google Cloud Platform storage, big data, and analytics services covered by the exam. We examine concepts related to data transformation, real-time processing, visualization, and machine learning and best practices to solve common problems.
Google Cloud Professional Data Engineer
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
Data Integration with Cloud Data Fusion
2 DAYS
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
Managing a Data Mesh with Dataplex
2 DAYS
Dataplex is an intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts to power analytics at scale. Specifically, you can use Dataplex to build a data mesh architecture, which is an organizational and technical approach that decentralizes data ownership among domain data owners.
In this course, you will learn how to discover, manage, monitor, and govern your data across data lakes, data warehouses, and data marts through guided lecture and independent exercises using sample data.
Developing Applications with Google Cloud
3 DAYS
In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
Enterprise Database Migration
4 DAYS
This course is intended to give architects, engineers, and developers the skills required to 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 Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
