Data Engineering on Google Cloud
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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
- Experience with developing applications using a common programming language such as Python
- Familiarity with Machine Learning and/or statistics
All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer.
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
-What’s 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 AutoML
-Why Auto ML?
-Auto ML Vision
-Auto ML NLP
-Auto ML Tables
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