Data Engineering on Google Cloud

Gain a hands-on introduction to designing and building data processing systems on Google Cloud with this four-day instructor-led course.
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4 day course
Supporting material
Google Cloud Partner of the Year
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Jellyfish has recently been named a Google Cloud Specialisation Partner of the Year. This title recognises our commitment to provide world-leading Cloud-based Training solutions that help our clients succeed.

We've been selected by Google to facilitate the delivery of this four-day course. All of our trainers are experienced practitioners, so you can learn with total confidence.

Through a combination of presentations, demos, and hands-on labs, you will learn how to design data processing systems, build end-to-end data pipelines, analyse data and carry out machine learning. The course covers structured, unstructured, and streaming data.

This course will be run over four consecutive days, and is delivered as a private training session that can be at run at a location of your choice.

Course overview
Who should attend:
This course 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, visualising query results and creating reports
Walk away with the ability to:
  • Design and build data processing systems on Google Cloud
  • 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
To get the most of out of this course, you 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
Course agenda
Module 1: Introduction to Data Engineering
  • Explore the role of a data engineer
  • Analyse 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 GC customer case study
  • Lab: Analysing Data with BigQuery
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
  • Optional Demo: Optimising 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
  • Optimising 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 Google Cloud 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: Optimising 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 Google Cloud
  • 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 AutoML
  • Why Auto ML?
  • Auto ML Vision
  • Auto ML NLP
  • Auto ML Tables
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