Data Engineering with Google Cloud Specialization

Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). 

Overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

Obtained the following skills: 

• 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

The two key components of any data pipeline are data lakes and warehouses. 

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. 

Understanding 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. 

Hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs. 

Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. 

Understand which paradigm should be used and when for batch data. 

Covered 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. 

Hands-on experience building data pipeline components on Google Cloud Platform using QwikLabs. 

Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. 

Build streaming data pipelines on Google Cloud Platform. Cloud Pub/Sub is described for handling incoming streaming data. 

Apply aggregations and transformations to streaming data using Cloud Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. 

Hands-on experience building streaming data pipeline components on Google Cloud Platform using QwikLabs. 

Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data.

Cover the 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. 

How to productionalize machine learning solutions using Kubeflow. 

Hands-on experience building machine learning models on Google Cloud Platform using QwikLabs. 

Top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. 

Create a custom preparation plan. 

Distinguish what you know from what you don't know. Develop and practice skills required to perform this job. 

Follows the organization of the Exam Guide outline, presenting highest-level concepts, "touchstones". Learn and 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. 

Completed the graded practice exam quiz that simulates the exam-taking experience.