Data Engineering on Google Cloud Training

Get hands-on experience 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, and analyze data. This course covers structured, unstructured, and streaming data.

Products:

• BigQuery
• Bigtable
• Cloud Storage
• Cloud SQL
• Spanner
• Dataproc
• Dataflow
• Cloud Data Fusion
• Cloud Composer
• Pub/Sub

Participants should have:

• 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

Target audience

This course is designed for:

• Data engineers
• Database administrators
• System administrators

By the end of this course, learners will be able to:

• 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.

Module 01: Data engineering tasks and components

• 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

Module 02: Data replication and migration

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

Module 03: The extract and load data pipeline pattern

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

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

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

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

• 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

Module 06: Automation techniques

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

Module 07: Introduction to data engineering

• 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

Module 08: Build a Data Lake

• 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

Module 09: Build a data warehouse

• 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

Module 10: Introduction to building batch data pipelines

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

Module 11: Execute Spark on Dataproc

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

Module 12: Serverless data processing with Dataflow

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

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

• Build batch data pipelines visually with Cloud Data Fusion
• Components
• 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

Module 14: Serverless messaging with Pub/Sub

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

Module 16: Dataflow streaming features

• Streaming data challenges
• Dataflow windowing

Module 17: High-throughput BigQuery and Bigtable streaming features

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

Module 18: Advanced BigQuery functionality and performance

• Analytic window functions
• GIS functions
• Performance considerations

Exams and assessments

There is no specific certification related to this course.

Hands-on learning

There are practical labs in this course.