Overview

This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

Audience:

This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, Loading, Transforming, cleaning, and validating data Designing pipelines and architectures for data processing Creating and maintaining machine learning and statistical models Querying datasets, visualizing query results and creating reports To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals: Big Data & Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such as Python Familiarity with Machine Learning and/or statistics

Read more

Prerequisites

To get the most of out of this course, participants should have: Completed: Google Cloud Platform Fundamentals: Core Infrastructure course OR have equivalent experience. Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such as Python Familiarity with basic statistics

Read more

Learning Outcomes

This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML Enable instant insights from streaming data

Read more

Course Outline

Module 1: Introduction to Data Engineering

  • Explore the role of a data engineer.
  • Analyze data engineering challenges.
  • Intro to BigQuery.
  • Data Lakes and Data Warehouses.
  • Demo: Federated Queries with BigQuery.
  • Transactional Databases vs Data Warehouses.
  • Website Demo: Finding PII in your dataset with DLP API.
  • Partner effectively with other data teams.
  • Manage data access and governance.
  • Build production-ready pipelines.
  • Review GCP customer case study.
  • Lab: Analyzing Data with BigQuery.

Module 2: Building a Data Lake

  • Introduction to Data Lakes.
  • Data Storage and ETL options on GCP.
  • Building a Data Lake using Cloud Storage.
  • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
  • Securing Cloud Storage.
  • Storing All Sorts of Data Types.
  • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
  • Cloud SQL as a relational Data Lake.
  • Lab: Loading Taxi Data into Cloud SQL.

Module 3: Building a Data Warehouse

  • The modern data warehouse.
  • Intro to BigQuery.
  • Demo: Query TB+ of data in seconds.
  • Getting Started.
  • Loading Data.
  • Video Demo: Querying Cloud SQL from BigQuery.
  • Lab: Loading Data into BigQuery.
  • Exploring Schemas.
  • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
  • Schema Design.
  • Nested and Repeated Fields.
  • Demo: Nested and repeated fields in BigQuery.
  • Lab: Working with JSON and Array data in BigQuery.
  • Optimizing with Partitioning and Clustering.
  • Demo: Partitioned and Clustered Tables in BigQuery.
  • Preview: Transforming Batch and Streaming Data.

Module 4: Introduction to Building Batch Data Pipelines

  • EL, ELT, ETL.
  • Quality considerations.
  • How to carry out operations in BigQuery.
  • Demo: ELT to improve data quality in BigQuery.
  • Shortcomings.
  • ETL to solve data quality issues.

Module 5: Executing Spark on Cloud Dataproc

  • The Hadoop ecosystem.
  • Running Hadoop on Cloud Dataproc.
  • GCS instead of HDFS.
  • Optimizing Dataproc.
  • Lab: Running Apache Spark jobs on Cloud Dataproc.

Module 6: Serverless Data Processing with Cloud Dataflow

  • Cloud Dataflow.
  • Why customers value Dataflow.
  • Dataflow Pipelines.
  • Lab: A Simple Dataflow Pipeline (Python/Java).
  • Lab: MapReduce in Dataflow (Python/Java).
  • Lab: Side Inputs (Python/Java).
  • Dataflow Templates.
  • Dataflow SQL.

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.
  • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
  • Orchestrating work between GCP services with Cloud Composer.
  • Apache Airflow Environment.
  • DAGs and Operators.
  • Workflow Scheduling.
  • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
  • Monitoring and Logging.
  • Lab: An Introduction to Cloud Composer.

Module 8: Introduction to Processing Streaming Data

  • Processing Streaming Data.

Module 9: Serverless Messaging with Cloud Pub/Sub

  • Cloud Pub/Sub.
  • Lab: Publish Streaming Data into Pub/Sub.

Module 10: Cloud Dataflow Streaming Features

  • Cloud Dataflow Streaming Features.
  • Lab: Streaming Data Pipelines.

Module 11: High-Throughput BigQuery and Bigtable Streaming Features

  • BigQuery Streaming Features.
  • Lab: Streaming Analytics and Dashboards.
  • Cloud Bigtable.
  • Lab: Streaming Data Pipelines into Bigtable.

Module 12: Advanced BigQuery Functionality and Performance

  • Analytic Window Functions.
  • Using With Clauses.
  • GIS Functions.
  • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
  • Performance Considerations.
  • Lab: Optimizing your BigQuery Queries for Performance.
  • Optional Lab: Creating Date-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 GCP.

Module 14: Prebuilt ML model APIs for Unstructured Data

  • Unstructured Data is Hard.
  • ML APIs for Enriching Data.
  • Lab: Using the Natural Language API to Classify Unstructured Text.

Module 15: Big Data Analytics with Cloud AI Platform Notebooks

  • Whats a Notebook.
  • BigQuery Magic and Ties to Pandas.
  • Lab: BigQuery in Jupyter Labs on AI Platform.

Module 16: Production ML Pipelines with Kubeflow

  • Ways to do ML on GCP.
  • Kubeflow.
  • AI Hub.
  • Lab: Running AI models on Kubeflow.


Module 17: Custom Model building with SQL in BigQuery ML

  • BigQuery ML for Quick Model Building.
  • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
  • Supported Models.
  • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
  • Lab Option 2: Movie Recommendations in BigQuery ML.


Module 18: Custom Model building with Cloud AutoMLW

  • Why Auto ML?
  • Auto ML Vision.
  • Auto ML NLP.
  • Auto ML Tables.
Read more

Why choose QA

Dates & Locations

GCP learning paths

Want to boost your career in Google Cloud Platform? Click on the roles below to see QA‘s learning pathways, specially designed to give you the skills to succeed.

= Required
= Certification
Data Scientist
Data Engineer
Developer Software Development Experience
Architect Enterprise Architecture Experience
Administrator Windows Administrator Experience

Frequently asked questions

See all of our FAQs

How can I create an account on myQA.com?

There are a number of ways to create an account. If you are a self-funder, simply select the "Create account" option on the login page.

If you have been booked onto a course by your company, you will receive a confirmation email. From this email, select "Sign into myQA" and you will be taken to the "Create account" page. Complete all of the details and select "Create account".

If you have the booking number you can also go here and select the "I have a booking number" option. Enter the booking reference and your surname. If the details match, you will be taken to the "Create account" page from where you can enter your details and confirm your account.

Find more answers to frequently asked questions in our FAQs: Bookings & Cancellations page.

How do QA’s virtual classroom courses work?

Our virtual classroom courses allow you to access award-winning classroom training, without leaving your home or office. Our learning professionals are specially trained on how to interact with remote attendees and our remote labs ensure all participants can take part in hands-on exercises wherever they are.

We use the WebEx video conferencing platform by Cisco. Before you book, check that you meet the WebEx system requirements and run a test meeting (more details in the link below) to ensure the software is compatible with your firewall settings. If it doesn’t work, try adjusting your settings or contact your IT department about permitting the website.

Learn more about our Virtual Classrooms.

How do QA’s online courses work?

QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.

All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.

Learn more about QA’s online courses.

When will I receive my joining instructions?

Joining instructions for QA courses are sent two weeks prior to the course start date, or immediately if the booking is confirmed within this timeframe. For course bookings made via QA but delivered by a third-party supplier, joining instructions are sent to attendees prior to the training course, but timescales vary depending on each supplier’s terms. Read more FAQs.

When will I receive my certificate?

Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.

Contact Us

Please contact us for more information