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Gayatri Panda

Date d'abonnement : 2022

Ligue de bronze

1690 points
Data Warehousing for Partners: Design in BigQuery Earned juil. 16, 2024 EDT
Data Lake Modernization on Google Cloud: Cloud Composer Earned août 5, 2023 EDT
[DEPRECATED] Data Engineering Earned mars 6, 2022 EST
Preparing for your Professional Data Engineer Journey Earned mars 1, 2022 EST
Smart Analytics, Machine Learning, and AI on Google Cloud Earned fév. 23, 2022 EST
Build Streaming Data Pipelines on Google Cloud Earned fév. 21, 2022 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned fév. 8, 2022 EST
Google Cloud Essentials Earned fév. 4, 2022 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned fév. 3, 2022 EST

Welcome to Design in BigQuery, where we map Enterprise Data Warehouse concepts and components to BigQuery and Google data services with a focus on schema design.

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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 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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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

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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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