Overview

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 for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.

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Outline

Introduction to Building Batch Data Pipelines

This module reviews different methods of data loading: EL, ELT and ETL and when to use what

  • Module introduction
  • EL, ELT, ETL
  • Quality considerations
  • How to carry out operations in BigQuery
  • Shortcomings
  • ETL to solve data quality issues
  • QUIZ
  • Introduction to Building Batch Data Pipelines

Executing Spark on Dataproc

This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.

  • Module introduction
  • The Hadoop ecosystem
  • Running Hadoop on Dataproc
  • Cloud Storage instead of HDFS
  • Optimizing Dataproc
  • Optimizing Dataproc storage
  • Optimizing Dataproc templates and autoscaling
  • Optimizing Dataproc monitoring
  • Lab Intro: Running Apache Spark jobs on Dataproc
  • LAB: Running Apache Spark jobs on Cloud Dataproc: This lab focuses on running Apache Spark jobs on Cloud Dataproc.
  • Summary
  • QUIZ

Serverless Data Processing with Dataflow

This module covers using Dataflow to build your data processing pipelines

  • Module introduction
  • Introduction to Dataflow
  • Why customers value Dataflow
  • Building Dataflow pipelines in code
  • Key considerations with designing pipelines
  • Transforming data with PTransforms
  • Lab Intro: Building a Simple Dataflow Pipeline
  • LAB: A Simple Dataflow Pipeline (Python) 2.5: In this lab, you learn how to write a simple Dataflow pipeline and run it both locally and on the cloud.
  • LAB: Serverless Data Analysis with Dataflow: A Simple Dataflow Pipeline (Java): In this lab you will open a Dataflow project, use pipeline filtering, and execute the pipeline locally and on the cloud using Java.
  • Aggregate with GroupByKey and Combine
  • Lab Intro: MapReduce in Beam
  • LAB: MapReduce in Beam (Python) 2.5: In this lab, you learn how to use pipeline options and carry out Map and Reduce operations in Dataflow.
  • LAB: Serverless Data Analysis with Beam: MapReduce in Beam (Java): In this lab you will identify Map and Reduce operations, execute the pipeline, use command line parameters.
  • Side inputs and windows of data
  • Lab Intro: Practicing Pipeline Side Inputs
  • LAB: Serverless Data Analysis with Dataflow: Side Inputs (Python): In this lab you will try out a BigQuery query, explore the pipeline code, and execute the pipeline using Python.
  • LAB: Serverless Data Analysis with Dataflow: Side Inputs (Java): In this lab you will try out a BigQuery query, explore the pipeline code, and execute the pipeline using Java.
  • Creating and re-using pipeline templates
  • Summary
  • QUIZ

Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.

  • Module introduction
  • Introduction to Cloud Data Fusion
  • Components of Cloud Data Fusion
  • Cloud Data Fusion UI
  • Build a pipeline
  • Explore data using wrangler
  • Lab Intro: Building and executing a pipeline graph in Cloud Data Fusion
  • LAB: Building and Executing a Pipeline Graph with Data Fusion 2.5: This tutorial shows you how to use the Wrangler and Data Pipeline features in Cloud Data Fusion to clean, transform, and process taxi trip data for further analysis.
  • Orchestrate work between Google Cloud services with Cloud Composer
  • Apache Airflow environment
  • DAGs and Operators
  • Workflow scheduling
  • Monitoring and Logging
  • Lab Intro: An Introduction to Cloud Composer
  • LAB: An Introduction to Cloud Composer 2.5: In this lab, you create a Cloud Composer environment using the GCP Console. You then use the Airflow web interface to run a workflow that verifies a data file, creates and runs an Apache Hadoop wordcount job on a Dataproc cluster, and deletes the cluster.
  • QUIZ

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