Data Engineering with Google Cloud Platform

(4 days)

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.

Objectives

This course teaches participants the following skills:

• Design and build data processing systems on Google Cloud Platform
• 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
• Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
• Enable instant insights from 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

Prerequisites

To get the most of out of this course, participants should have:

• Completed Google Cloud Fundamentals- Big Data and 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

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Course Outline

Day 1: Modernizing Data Lakes and Data Warehouses with GCP

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 with Console and CLI
• 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: ARRAYs and STRUCTs
• Optimizing with Partitioning and Clustering
• Demo: Partitioned and Clustered Tables in BigQuery
• Preview: Transforming Batch and Streaming Data

Day 2: Batch Processing of Data with Spark and Hadoop on GCP

Module 1 – 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 2 – 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 3 – 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 4: 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

Day 3: Building Resilient Streaming Analytics Systems on GCP

Module 1: Introduction to Processing Streaming Data

• Processing Streaming Data

Module 2: Serverless Messaging with Cloud Pub/Sub

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

Module 3: Cloud Dataflow Streaming Features

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

Module 4: High-Throughput BigQuery and Bigtable Streaming Features

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

Module 5: 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

Day 4: Smart Analytics, Machine Learning and AI on GCP

Module 1: Introduction to Analytics and AI

• What is AI?
• From Ad-hoc Data Analysis to Data Driven Decisions
• Options for ML models on GCP

Module 2: 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 3: Big Data Analytics with Cloud AI Platform Notebooks

• What’s a Notebook
• BigQuery Magic and Ties to Pandas
• Lab: BigQuery in Jupyter Labs on AI Platform

Module 4: Production ML Pipelines with Kubeflow

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

Module 5: 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 6: Custom Model building with Cloud AutoML

• Why Auto ML?
• Auto ML Vision
• Auto ML NLP
• Auto ML Tables

** Notice: Cancellations will be charged an administrative fee through Eventbrite.