Course Overview

  • icon4 day course
  • iconPartner of the Year
  • iconPrivate
    info-icon
  • iconCertificate of Attendance

As a Google Cloud Partner, we’ve been selected to deliver this four-day Data Engineering course.

Our expert Cloud trainers will combine presentations and demos with practical, lab-orientated workshops to teach you the steps required to design data processing systems. You’ll learn how to build end-to-end data pipelines, analyze data and carry out machine learning.

The course covers structured, unstructured, and streaming data, so if you’re a budding Data Engineer, you’ll leave with the ability and confidence to be able to apply your new skills to a variety of scenarios and datasets.

Our Data Engineering on Google Cloud course is delivered via Virtual Classroom. We also offer it as a private training session that can be delivered virtually or at a location of your choice in the UK.

This course is an intermediate-level course. If you’re looking to master the basics, you would benefit from our Google Cloud Fundamentals: Big Data & Machine Learning course.

Want to combine the two? Take a look at our Professional Data Engineer track, which will get you on track to obtaining the Google Data Engineer certification.

What you'll learn

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

  • iconDesign and build data processing systems on Google Cloud
  • iconDerive business insights from extremely large datasets using
  • iconLeverage unstructured data using Spark and ML APIs on Dataproc
  • iconUnderstand ML APIs and BigQuery ML, and learn to use AutoML to create powerful models without coding
  • iconProcess batch and streaming data by implementing autoscaling data pipelines on Dataflow
  • iconBigQuery
  • iconEnable instant insights from streaming data

Course agenda

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
  • 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

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
  • 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

Module 5: Executing Spark on Dataproc

  • The Hadoop ecosystem
  • Run Hadoop on Dataproc
  • Cloud Storage instead of HDFS
  • Optimize Dataproc
  • 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
  • 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 & 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
  • 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
  • Lab: Publish Streaming Data into Pub / Sub

Module 10: Dataflow Streaming Features

  • Steaming data challenges
  • Dataflow windowing
  • Lab: Streaming Data Pipelines

Module 11: High-Throughput BigQuery & Bigtable Streaming Features

  • Streaming into BigQuery and visualizing results
  • High-throughput streaming with Cloud Bigtable
  • Optimizing Cloud Bigtable performance
  • Lab: Streaming Analytics and Dashboards
  • Lab: Streaming Data Pipelines into Bigtable

Module 12: Advanced BigQuery Functionality & Performance

  • Analytic window functions
  • Use With clauses
  • GIS functions
  • Performance considerations
  • Lab: Optimizing your BigQuery Queries for Performance
  • Optional Lab: Partitioned Tables in BigQuery

Module 13: Introduction to Analytics & AI

  • What is AI?
  • From ad-hoc data analysis to data-driven decisions
  • Options for ML models on Google Cloud

Module 14: Prebuilt ML Model APIs for Unstructured Data

  • Challenges dealing with unstructured data
  • ML APIs for enriching 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
  • 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
  • Lab: Running Pipelines on Vertex AI

Module 17: Custom Model Building with SQL in BigQuery ML

  • BigQuery ML for quick model building
  • Supported models
  • 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

Who it's for

This course 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

Prerequisites

To benefit from this course, participants should have completed our Google Cloud Fundamentals: Big Data & Machine Learning course or have equivalent experience. You 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

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