Google Cloud Platform Big Data and Machine Learning Fundamentals
This 2-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

At the end of this course, participants will be able to: * Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform * Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform * Employ BigQuery and Cloud Datalab to carry out interactive data analysis * Choose between Cloud SQL, BigTable and Datastore * Train and use a neural network using TensorFlow * Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: * A common query language such as SQL * Extract, transform, load activities * Data modeling * Machine learning and/or statistics * Programming in Python Google Account Notes: * Google services are currently unavailable in China.

Modernizing Data Lakes and Data Warehouses with GCP
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud Platform in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. Learners will get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs.

Building Batch Data Pipelines on GCP
Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud Platform for data transformation including BigQuery, executing Spark on Cloud Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Cloud Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud Platform using QwikLabs.

Building Resilient Streaming Analytics Systems on GCP
Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud Platform using QwikLabs.

Smart Analytics, Machine Learning, and AI on GCP
Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud Platform depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces AI Platform Notebooks and BigQuery Machine Learning. Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud Platform using QwikLabs.

Preparing for the Google Cloud Professional Data Engineer Exam
From the course: "The best way to prepare for the exam is to be competent in the skills required of the job."
This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job. The course follows the organization of the Exam Guide outline, presenting highest-level concepts, "touchstones", for you to determine whether you feel confident about your knowledge of that area and its dependent concepts, or if you want more study. You also will learn about and have the opportunity to practice key job skills, including cognitive skills such as case analysis, identifying technical watchpoints, and developing proposed solutions. These are job skills that are also exam skills. You will also test your basic abilities with Activity Tracking Challenge Labs. And you will have many sample questions similar to those on the exam, including solutions. The end of the course contains an ungraded practice exam quiz, followed by a graded practice exam quiz that simulates the exam-taking experience.

Citación :

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