T-IDEGC-BOfficial Google Curriculum
Introduction to Data Engineering on Google Cloud
1 dayILTIntroductoryLoading...
Overview
Learn about data engineering on Google Cloud, roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud.
What You'll Learn
- Understand the role of a data engineer
- Identify data engineering tasks and core components used on Google Cloud
- Understand how to create and deploy data pipelines of varying patterns on Google Cloud
- Identify and utilize various automation techniques on Google Cloud
Who Should Attend
Data engineers, Database administrators, System administrators
Prerequisites
Prior Google Cloud experience at the fundamental level 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.
Products Covered
Analytics HubBigQueryStorage Transfer ServiceTransfer ApplianceDatastreamBigQuery Data Transfer ServiceBigLakeDataformDataprocBigtableDataflowCloud SchedulerWorkflowsCloud ComposerCloud Run functionsEventarc
Course Modules
1
Data Engineering Tasks and Components
Topics
- The role of a data engineer
- Data sources versus data sinks
- Data formats
- Storage solution options on Google Cloud
- Metadata management options on Google Cloud
- Sharing datasets using Analytics Hub
Learning Outcomes
- 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 or the gcloud CLI
Activities
Lab: Loading Data into BigQueryQuiz
2
Data Replication and Migration
Topics
- Replication and migration architecture
- The gcloud command-line tool
- Moving datasets
- Datastream
Learning Outcomes
- 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 Storage Transfer Service
- Explain the functionality and use cases for Transfer Appliance
- Understand the features and deployment of Datastream
Activities
Lab: Datastream: PostgreSQL Replication to BigQuery (optional for ILT)Quiz
3
The Extract and Load Data Pipeline Pattern
Topics
- Extract and load architecture
- The bq command-line tool
- BigQuery Data Transfer Service
- BigLake
Learning Outcomes
- Explain the baseline extract and load architecture diagram
- Understand the options of the bq command-line tool
- Explain the functionality and use cases for BigQuery Data Transfer Service
- Explain the functionality and use cases for BigLake as a non-extract-load pattern
Activities
Lab: BigLake: Qwik StartQuiz
4
The Extract, Load, and Transform Data Pipeline Pattern
Topics
- Extract, load, and transform (ELT) architecture
- SQL scripting and scheduling with BigQuery
- Dataform
Learning Outcomes
- 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 DataformQuiz
5
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
Learning Outcomes
- 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 how 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 (optional for ILT)Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with DataflowQuiz
6
Automation Techniques
Topics
- Automation patterns and options for pipelines
- Cloud Scheduler and Workflows
- Cloud Composer
- Cloud Run Functions
- Eventarc
Learning Outcomes
- 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 (optional for ILT)Quiz
What's Not Covered
- Detailed data pipeline development
- Detailed usage of metadata management tools
- Detailed coverage of automation practices
Get This Training
No public classes currently scheduled. Express interest below or request private training.
Course Details
- Course Code
- T-IDEGC-B
- Duration
- 1 day
- Format
- ILT
- Level
- Introductory
- Modules
- 6
- Activities
- 13
- Price
- Loading...
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