Data Engineering on Google Cloud Platform

Paths

Data Engineering on Google Cloud Platform

Author: Google Cloud

This path provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos,... Read more

What you will learn

This path teaches the following skills

  • Design and build data processing systems on Google Cloud Platform
  • Lift and shift your existing Hadoop workloads to the Cloud using Cloud Dataproc.
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
  • Manage your data Pipelines with Data Fusion and Cloud Composer.
  • Derive business insights from extremely large datasets using Google BigQuery
  • Learn how to use pre-built ML APIs on unstructured data and build different kinds of ML models using BigQuery ML.
  • Enable instant insights from streaming data

Pre-requisites

Participants should have experience with one or more of the following:

• A common query language such as SQL • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Integrating analytics and machine learning capabilities into data pipelines • Querying datasets, visualizing query results and creating reports

Beginner

This section 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.

Google Cloud Platform Big Data and Machine Learning Fundamentals

by Google Cloud

Aug 3, 2019 / 4h 54m

4h 54m

Start Course
Description

This 1-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.

Table of contents
  1. Course Overview
  2. Introduction to Google Cloud Platform
  3. Recommending Products using Cloud SQL and Spark
  4. Predict Visitor Purchases with BigQuery ML
  5. Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow
  6. Classify Images with Pre-Built Models using Vision API and Cloud AutoML
  7. Summary

Intermediate

This section opens with the two key components of any data pipeline, which are data lakes and warehouses. The first 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, the 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. Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load paradigms. Hence, the second course in this section 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.

Modernizing Data Lakes and Data Warehouses with GCP

by Google Cloud

Jan 14, 2020 / 3h 35m

3h 35m

Start Course
Description

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.

Table of contents
  1. Introduction
  2. Introduction to Data Engineering
  3. Building a Data Lake
  4. Building a data warehouse
  5. Summary

Building Batch Data Pipelines on GCP

by Google Cloud

Jan 14, 2020 / 2h 43m

2h 43m

Start Course
Description

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.

Table of contents
  1. Introduction
  2. Introduction to Batch Data Pipelines
  3. Executing Spark on Cloud Dataproc
  4. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
  5. Serverless Data Processing with Cloud Dataflow
  6. Summary

Advanced

This section covers two things: (ii) Processing streaming data, which is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations, and (ii) Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. The first 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. The second 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.

Building Resilient Streaming Analytics Systems on GCP

by Google Cloud

Jan 14, 2020 / 3h 12m

3h 12m

Start Course
Description

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.

Table of contents
  1. Introduction
  2. Introduction to Processing Streaming Data
  3. Serverless Messaging with Cloud Pub/Sub
  4. Cloud Dataflow Streaming Features
  5. High-Throughput BigQuery and Bigtable Streaming Features
  6. Advanced BigQuery Functionality and Performance
  7. Summary

Smart Analytics, Machine Learning, and AI on GCP

by Google Cloud

Jan 14, 2020 / 1h 40m

1h 40m

Start Course
Description

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.

Table of contents
  1. Introduction
  2. Introduction to Analytics and AI
  3. Prebuilt ML model APIs for Unstructured Data
  4. Big Data Analytics with Cloud AI Platform Notebooks
  5. Productionizing Custom ML Models
  6. Custom Model building with SQL in BigQuery ML
  7. Custom Model Building with Cloud AutoML
  8. Summary