Understanding LookML in Looker
Earned oct. 27, 2023 EDT
Prepare Data for Looker Dashboards and Reports
Earned oct. 26, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned oct. 26, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned oct. 25, 2023 EDT
Developing Data Models with LookML
Earned oct. 24, 2023 EDT
Build a Data Warehouse with BigQuery
Earned oct. 22, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned oct. 20, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned oct. 19, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned oct. 13, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 29, 2023 EDT
This course provides a holistic experience of optimally configuring SAP on Google Cloud. Participants will learn to configure SAP on Google Cloud, and what best practices are, leaving the course with actionable experience to configure SAP on Google Cloud and run SAP workloads on Google Cloud.
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In this beginner-level course, you will learn about the Data Analytics workflow on Google Cloud and the tools you can use to explore, analyze, and visualize data and share your findings with stakeholders. Using a case study along with hands-on labs, lectures, and quizzes/demos, the course will demonstrate how to go from raw datasets to clean data to impactful visualizations and dashboards. Whether you already work with data and want to learn how to be successful on Google Cloud, or you’re looking to progress in your career, this course will help you get started. Almost anyone who performs or uses data analysis in their work can benefit from this course.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.
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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.
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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. 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.
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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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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.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
Deploying SAP on Google Cloud
Earned fév. 1, 2024 EST
Introduction to Data Analytics on Google Cloud
Earned déc. 22, 2023 EST
Introduction to Generative AI
Earned déc. 12, 2023 EST
Preparing for your Professional Data Engineer Journey
Earned oct. 31, 2023 EDT
Applying Advanced LookML Concepts in Looker
Earned oct. 27, 2023 EDT
Understanding LookML in Looker
Earned oct. 27, 2023 EDT
Prepare Data for Looker Dashboards and Reports
Earned oct. 26, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned oct. 26, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned oct. 25, 2023 EDT
Developing Data Models with LookML
Earned oct. 24, 2023 EDT
Build a Data Warehouse with BigQuery
Earned oct. 22, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned oct. 20, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned oct. 19, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned oct. 13, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 29, 2023 EDT