Google Cloud Big Data and Machine Learning Fundamentals
Earned sept. 18, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 7, 2022 EDT
This course continues to explore the implementation of data load and transformation pipelines for a BigQuery Data Warehouse using Cloud Data Fusion.
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Welcome to Cloud Composer, where we discuss how to orchestrate data lake workflows with Cloud Composer.
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This course explores how to implement a streaming analytics solution using Pub/Sub.
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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.
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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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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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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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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.
Data Warehousing for Partners: Cloud Data Fusion Pipelines
Earned juin 16, 2023 EDT
Data Lake Modernization on Google Cloud: Cloud Composer
Earned mai 22, 2023 EDT
Data Warehousing for Partners: Stream Data with Pub/Sub
Earned mars 30, 2023 EDT
Build Infrastructure with Terraform on Google Cloud
Earned nov. 11, 2022 EST
Set Up an App Dev Environment on Google Cloud
Earned oct. 25, 2022 EDT
Prepare Data for ML APIs on Google Cloud
Earned sept. 25, 2022 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned sept. 22, 2022 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned sept. 19, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned sept. 18, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 7, 2022 EDT