Data Engineering on Google Cloud

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.

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Duration 4 days
Level intermediate
Format Instructor led, On-demand

What you'll learn

  • 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.
  • Understand ML APIs and BigQuery ML, and learn to use AutoML to create powerful models without coding.

About this course


18 Modules · 143 Videos · 24 Labs · 21 Classrom activities

Who this course is for

This class is intended for developers who are responsible for:

  • 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.

To benefit from this course, participants should have completed “Google Cloud Big Data and Machine Learning Fundamentals” or have equivalent experience. Participant should also have:

  • Basic proficiency with a common query language such as SQL.
  • Experience with data modeling and ETL (extract, transform, load) activities.
  • Experience with developing applications using a common programming language such as Python.
  • Familiarity with machine learning and/or statistics.
  • BigQuery
  • Cloud Bigtable
  • Cloud Storage
  • Cloud SQL
  • Cloud Spanner
  • Dataproc
  • Dataflow
  • Cloud Data Fusion
  • Cloud Composer
  • Pub/Sub
  • Vertex AI
  • Cloud ML APIs
Module 1
Introduction to Data Engineering
  • Explore the role of a data engineer
  • Analyze data engineering challenges
  • Introduction to BigQuery
  • Data lakes and data warehouses
  • Transactional databases versus data warehouses
  • Partner effectively with other data teams
  • Manage data access and governance
  • Build production-ready pipelines
  • Review Google Cloud customer case study
  • Understand the role of a data engineer
  • Discuss benefits of doing data engineering in the cloud
  • Discuss challenges of data engineering practice 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

Lab: Using BigQuery to do Analysis

Module 2
Building a Data Lake
  • Introduction to data lakes
  • Data storage and ETL options on Google Cloud
  • Building a data lake using Cloud Storage
  • Securing Cloud Storage
  • Storing all sorts of data types
  • Cloud SQL as a relational data lake
  • Understand why Cloud Storage is a great option for building a data lake on Google Cloud
  • Learn how to use Cloud SQL for a relational data lake

Lab: Loading Taxi Data into Cloud SQL

Module 3
Building a Data Warehouse
  • The modern data warehouse
  • Introduction to BigQuery
  • Getting started with BigQuery
  • Loading data
  • Exploring schemas
  • Schema design
  • Nested and repeated fields
  • Optimizing with partitioning and clustering
  • Discuss requirements of a modern warehouse
  • Understand why BigQuery is the scalable data warehousing solution on Google Cloud
  • Understand core concepts of BigQuery and review options of loading data into BigQuery
  • Lab: Loading Data into BigQuery
  • Lab: Working with JSON and Array Data in BigQuery
Module 4
Introduction to Building Batch Data Pipelines
  • EL, ELT, ETL
  • Quality considerations
  • How to carry out operations in BigQuery
  • Shortcomings
  • ETL to solve data quality issues
  • Review different methods of loading data into your data lakes and warehouses: EL, ELT, and ETL
  • Discuss data quality considerations and when to use ETL instead of EL and ELT
Module 5
Executing Spark on Dataproc
  • The Hadoop ecosystem
  • Run Hadoop on Dataproc
  • Cloud Storage instead of HDFS
  • Optimize Dataproc
  • Review the parts of the Hadoop ecosystem
  • Learn how to lift and shift your existing Hadoop workloads to the cloud using Dataproc
  • Understand considerations around using Cloud Storage instead of HDFS for storage
  • Learn how to optimize Dataproc jobs

Lab: Running Apache Spark jobs on Dataproc

Module 6
Serverless Data Processing with Dataflow
  • Introduction to Dataflow
  • Why customers value Dataflow
  • Dataflow pipelines
  • Aggregating with GroupByKey and Combine
  • Side inputs and windows
  • Dataflow templates
  • Dataflow SQL
  • Understand how to decide between Dataflow and Dataproc for processing data pipelines
  • Understand the features that customers value in Dataflow
  • Discuss core concepts in Dataflow
  • Review the use of Dataflow templates and SQL
  • Lab: A Simple Dataflow Pipeline (Python/Java)
  • Lab: MapReduce in Dataflow (Python/Java)
  • Lab: Side inputs (Python/Java)
Module 7
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
  • Building batch data pipelines visually with Cloud Data Fusion
  • Components
  • UI overview
  • Building a pipeline
  • Exploring data using Wrangler
  • Orchestrating work between Google Cloud services with Cloud Composer
  • Apache Airflow environment
  • DAGs and operators
  • Workflow scheduling
  • Monitoring and logging
  • Discuss how to manage your data pipelines with Data Fusion and Cloud Composer
  • Understand Data Fusion’s visual design capabilities
  • Learn how Cloud Composer can help to orchestrate the work across multiple Google Cloud services
  • Lab: Building and Executing a Pipeline Graph in Data Fusion
  • Optional Lab: An introduction to Cloud Composer
Module 8
Introduction to Processing Streaming Data

Process Streaming Data

  • Explain streaming data processing
  • Describe the challenges with streaming data
  • Identify the Google Cloud products and tools that can help address streaming data challenges
Module 9
Serverless Messaging with Pub/Sub
  • Introduction to Pub/Sub
  • Pub/Sub push versus pull
  • Publishing with Pub/Sub code
  • Describe the Pub/Sub service
  • Understand how Pub/Sub works
  • Gain hands-on Pub/Sub experience with a lab that simulates real-time streaming sensor data

Lab: Publish Streaming Data into Pub/Sub

Module 10
Dataflow Streaming Features
  • Steaming data challenges
  • Dataflow windowing
  • Understand the Dataflow service
  • Build a stream processing pipeline for live traffic data
  • Demonstrate how to handle late data using watermarks, triggers, and accumulation

Lab: Streaming Data Pipelines

Module 11
High-Throughput BigQuery and Bigtable Streaming Features
  • Streaming into BigQuery and visualizing results
  • High-throughput streaming with Cloud Bigtable
  • Optimizing Cloud Bigtable performance
  • Learn how to perform ad hoc analysis on streaming data using BigQuery and dashboards
  • Understand how Cloud Bigtable is a low-latency solution
  • Describe how to architect for Bigtable and how to ingest data into Bigtable
  • Highlight performance considerations for the relevant services
  • Lab: Streaming Analytics and Dashboards
  • Lab: Streaming Data Pipelines into Bigtable
Module 12
Advanced BigQuery Functionality and Performance
  • Analytic window functions
  • Use With clauses
  • GIS functions
  • Performance considerations
  • Review some of BigQuery’s advanced analysis capabilities
  • Discuss ways to improve query performance
  • Lab: Optimizing your BigQuery Queries for Performance
  • Optional Lab: Partitioned Tables in BigQuery
Module 13
Introduction to Analytics and AI
  • What is AI?
  • From ad-hoc data analysis to data-driven decisions
  • Options for ML models on Google Cloud
  • Understand the proposition that ML adds value to your data
  • Understand the relationship between ML, AI, and Deep Learning
  • Identify ML options on Google Cloud
Module 14
Prebuilt ML Model APIs for Unstructured Data
  • Unstructured data is hard
  • ML APIs for enriching data
  • Discuss challenges when working with unstructured data
  • Learn the applications of ready to-use ML APIs on unstructured data

Lab: Using the Natural Language API to Classify Unstructured Text

Module 15
Big Data Analytics with Notebooks
  • What’s a notebook?
  • BigQuery magic and ties to Pandas
  • Introduce Notebooks as a tool for prototyping ML solutions
  • Learn to execute BigQuery commands from Notebooks

Lab: BigQuery in Jupyter Labs on AI Platform

Module 16
Production ML Pipelines
  • Ways to do ML on Google Cloud
  • Vertex AI Pipelines
  • AI Hub
  • Describe options available for building custom ML models
  • Understand the use of tools like Vertex AI Pipelines

Lab: Running Pipelines on Vertex AI

Module 17
Custom Model Building with SQL in BigQuery ML
  • BigQuery ML for quick model building
  • Supported models
  • Learn how to create ML models by using SQL syntax in BigQuery
  • Demonstrate building different kinds of ML models using BigQuery ML
  • Lab option 1: Predict Bike Trip Duration with a Regression Model in BigQuery ML
  • Lab option 2: Movie Recommendations in BigQuery ML
Module 18
Custom Model Building with AutoML
  • Why AutoML?
  • AutoML Vision
  • AutoML NLP
  • AutoML tables
  • Explore various AutoML products used in machine learning
  • Learn to use AutoML to create powerful models without coding

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