Description

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

Learning Objectives

• Design and build data processing systems on Google Cloud.
• Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
• Derive business insights from extremely large datasets using BigQuery.
• Leverage unstructured data using Spark and ML APIs on Dataproc.
• Enable instant insights from streaming data.

Prerequisites

• Prior Google Cloud experience using Cloud Shell and accessing products from the Google Cloud console.
• Basic proficiency with a common query language such as SQL.
• Experience with data modeling and ETL (extract, transform, load) activities.
• Experience developing applications using a common programming language such as Python

Who Should Attend

• Data engineers
• Database administrators
• System administrators

Course Outline

Module 01 – Data engineering tasks and components

Topics:

• The role of a data engineer
• Data sources versus data syncs
• Data formats
• Storage solution options on Google Cloud
• Metadata management options on Google Cloud
• Share datasets using Analytics Hub

Objectives:

• Explain the role of a data engineer.
• Understand the differences between a data source and a data sink.
• Explain the different types of data formats.
• Explain the storage solution options on Google Cloud.
• Learn about the metadata management options on Google Cloud.
• Understand how to share datasets with ease using Analytics Hub.
• Understand how to load data into BigQuery using the Google Cloud console and/or the gcloud CLI.

Activities:

• Lab: Loading Data into BigQuery

Module 02 – Data replication and migration

Topics:

• Replication and migration architecture
• The gcloud command line tool
• Moving datasets
• Datastream

Objectives:

• Explain the baseline Google Cloud data replication and migration architecture.
• Understand the options and use cases for the gcloud command line tool.
• Explain the functionality and use cases for the Storage Transfer Service.
• Explain the functionality and use cases for the Transfer Appliance.
• Understand the features and deployment of Datastream.

Activities:

• Lab: Datastream: PostgreSQL Replication to BigQuery

Module 03 – The extract and load data pipeline pattern

Topics:

• Extract and load architecture
• The bq command line tool
• BigQuery Data Transfer Service
• BigLake

Objectives:

• Explain the baseline extract and load architecture diagram.
• Understand the options of the bq command line tool.
• Explain the functionality and use cases for the BigQuery Data Transfer Service.
• Explain the functionality and use cases for BigLake as a non-extract-load pattern.

Activities:

• Lab: BigLake: Qwik Start

Module 04 – The extract, load, and transform data pipeline pattern

Topics:

• Extract, load, and transform (ELT) architecture
• SQL scripting and scheduling with BigQuery
• Dataform

Objectives:

• Explain the baseline extract, load, and transform architecture diagram.
• Understand a common ELT pipeline on Google Cloud.
• Learn about BigQuery’s SQL scripting and scheduling capabilities.
• Explain the functionality and use cases for Dataform.

Activities:

• Lab: Create and Execute a SQL Workflow in Dataform

Module 05 – The extract, transform, and load data pipeline pattern

Topics:

• Extract, transform, and load (ETL) architecture
• Google Cloud GUI tools for ETL data pipelines
• Batch data processing using Dataproc
• Streaming data processing options
• Bigtable and data pipelines

Objectives:

• Explain the baseline extract, transform, and load architecture diagram.
• Learn about the GUI tools on Google Cloud used for ETL data pipelines.
• Explain batch data processing using Dataproc.
• Learn to use Dataproc Serverless for Spark for ETL.
• Explain streaming data processing options.
• Explain the role Bigtable plays in data pipelines.

Activities:

• Lab: Use Dataproc Serverless for Spark to Load BigQuery
• Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

Module 06 – Automation techniques

Topics:

• Automation patterns and options for pipelines
• Cloud Scheduler and Workflows
• Cloud Composer
• Cloud Run functions
• Eventarc

Objectives:

• Explain the automation patterns and options available for pipelines.
• Learn about Cloud Scheduler and workflows.
• Learn about Cloud Composer.
• Learn about Cloud Run functions.
• Explain the functionality and automation use cases for Eventarc.

Activities:

• Lab: Use Cloud Run Functions to Load BigQuery

Module 07 – Introduction to data engineering

Topics:

• Data engineer’s role
• Data engineering challenges
• Introduction to BigQuery
• Data lakes and data warehouses
• Transactional databases versus data warehouses
• Effective partnership with other data teams
• Management of data access and governance
• Building of production-ready pipelines
• Google Cloud customer case study

Objectives:

• Discuss the challenges of data engineering, and how building data pipelines in the cloud helps to address these.
• Review and understand the purpose of a data lake versus a data warehouse, and when to use which.

Activities:

• Lab: Using BigQuery to Do Analysis

Module 08 – Build a Data Lake

Topics:

• Introduction to data lakes
• Data storage and ETL options on Google Cloud
• Building of a data lake using Cloud Storage
• Secure Cloud Storage
• Store all sorts of data types
• Cloud SQL as your OLTP system

Objectives:

• Discuss why Cloud Storage is a great option for building a data lake on Google Cloud.
• Explain how to use Cloud SQL for a relational data lake.

Activities:

• Lab: Loading Taxi Data into Cloud SQL

Module 09 – Build a data warehouse

Topics:

• The modern data warehouse
• Introduction to BigQuery
• Get started with BigQuery
• Loading of data into BigQuery
• Exploration of schemas
• Schema design
• Nested and repeated fields
• Optimization with partitioning and clustering

Objectives:

• Discuss requirements of a modern warehouse.
• Explain why BigQuery is the scalable data warehousing solution on Google Cloud.
• Discuss the core concepts of BigQuery and review options of loading data into BigQuery.

Activities:

• Lab: Working with JSON and Array Data in BigQuery
• Lab: Partitioned Tables in BigQuery

Module 10 – Introduction to building batch data pipelines

Topics:

• EL, ELT, ETL
• Quality considerations
• Ways of executing operations in BigQuery
• Shortcomings
• ETL to solve data quality issues

Objectives:

• Review different methods of loading data into your data lakes and warehouses: EL, ELT, and ETL.

Module 11 – Execute Spark on Dataproc

Topics:

• The Hadoop ecosystem
• Run Hadoop on Dataproc
• Cloud Storage instead of HDFS
• Optimize Dataproc

Objectives:

• Review the Hadoop ecosystem.
• Discuss how to lift and shift your existing Hadoop workloads to the cloud using Dataproc.
• Explain when you would use Cloud Storage instead of HDFS storage.
• Explain how to optimize Dataproc jobs.

Activities:

• Lab: Running Apache Spark Jobs on Dataproc

Module 12 – Serverless data processing with Dataflow

Topics:

• Introduction to Dataflow
• Reasons why customers value Dataflow
• Dataflow pipelines
• Aggregating with GroupByKey and Combine
• Side inputs and windows
• Dataflow templates

Objectives:

• Identify features customers value in Dataflow.
• Discuss core concepts in Dataflow.
• Review the use of Dataflow templates and SQL.
• Write a simple Dataflow pipeline and run it both locally and on the cloud.
• Identify Map and Reduce operations, execute the pipeline, and use command line parameters.
• Read data from BigQuery into Dataflow and use the output of a pipeline as a side-input to another pipeline.

Activities:

• Lab: A Simple Dataflow Pipeline (Python/Java)
• Lab: MapReduce in Beam (Python/Java)
• Lab: Side Inputs (Python/Java)

Module 13 – Manage data pipelines with Cloud Data Fusion and Cloud Composer

Topics:

• Build batch data pipelines visually with Cloud Data Fusion
  • Components
  • UI overview
  • Building a pipeline
  • Exploring data using Wrangler
• Orchestrate work between Google Cloud services with Cloud Composer
  • Apache Airflow environment
  • DAGs and operators
  • Workflow scheduling
  • Monitoring and logging

Objectives:

• Discuss how to manage your data pipelines with Cloud Data Fusion and Cloud Composer.
• Summarize how Cloud Data Fusion allows data analysts and ETL developers to wrangle data and build pipelines in a visual way.
• Describe how Cloud Composer can help to orchestrate the work across multiple Google Cloud services.

Activities:

• Lab: Building and Executing a Pipeline Graph in Data Fusion
• Lab: An Introduction to Cloud Composer

Module 14 – Introduction to processing streaming data

Topics:

• Process streaming data

Objectives:

• Explain streaming data processing.
• Identify the Google Cloud products and tools that can help address streaming data challenges.

Module 15 – Serverless messaging with Pub/Sub

Topics:

• Introduction to Pub/Sub
• Pub/Sub push versus pull
• Publishing with Pub/Sub code

Objectives:

• Describe the Pub/Sub service.
• Explain how Pub/Sub works.
• Simulate real-time streaming sensor data using Pub/Sub.

Activities:

• Lab: Publish Streaming Data into Pub/Sub

Module 16 – Dataflow streaming features

Topics:

• Steaming data challenges
• Dataflow windowing

Objectives:

• Describe the Dataflow service.
• Build a stream processing pipeline for live traffic data.
• Demonstrate how to handle late data using watermarks, triggers, and accumulation.

Activities:

• Lab: Streaming Data Pipelines

Module 17 – High-throughput BigQuery and Bigtable streaming features

Topics:

• Streaming into BigQuery and visualizing results
• High-throughput streaming with Bigtable
• Optimizing Bigtable performance

Objectives:

• Describe how to perform ad-hoc analysis on streaming data using BigQuery and dashboards.
• Discuss Bigtable as a low-latency solution.
• Describe how to architect for Bigtable and how to ingest data into Bigtable.
• Highlight performance considerations for the relevant services.

Activities:

• Lab: Streaming Analytics and Dashboards
• Lab: Generate Personalized Email Content with BigQuery Continuous Queries and Gemini
• Lab: Streaming Data Pipelines into Bigtable

Module 18 – Advanced BigQuery functionality and performance

Topics:

• Analytic window functions
• GIS functions
• Performance considerations

Objectives:

• Review some of BigQuery’s advanced analysis capabilities.
• Discuss ways to improve query performance.

Activities:

• Lab: Optimizing Your BigQuery Queries for Performance