[DEPRECATED] Data Engineering
Earned Nov 8, 2020 EST
Preparing for your Professional Data Engineer Journey
Earned Okt 25, 2020 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Okt 18, 2020 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Okt 16, 2020 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Okt 4, 2020 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Okt 3, 2020 EDT
This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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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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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.
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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.
[DEPRECATED] Data Engineering
Earned Nov 8, 2020 EST
Preparing for your Professional Data Engineer Journey
Earned Okt 25, 2020 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Okt 18, 2020 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Okt 16, 2020 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Okt 4, 2020 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Okt 3, 2020 EDT