Data Engineering on Google Cloud


Course Number: GCP-142

Duration: 4 days (26 hours)

Format: Live, hands-on

Google Cloud Data Training Overview

This instructor-led, hands-on Data Engineering on Google Cloud training course teaches attendees how to design and build data processing systems on Google Cloud.  Attendees learn how to design data processing systems, build end-to-end data pipelines, and analyze data. This class also covers structured, unstructured, and streaming data. 

Location and Pricing

This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.

In addition, some Cloud courses are available as live, online classes for individuals.

Objectives

  • Design and build data processing systems on the 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
  • Enable instant insights from streaming data

Prerequisites

  • Completed  Google Cloud Big Data and Machine Learning Fundamentals course or have equivalent experience
  • Basic proficiency with common query language such as SQL Experience with data modeling, extracting, transforming, and load activities
  • Experience developing applications using a common programming language such as Python
  • Familiarity with basic statistics

Outline

Expand All | Collapse All

Introduction to Data Engineering
  • Explore the role of a data engineer
  • Analyze data engineering challenges
  • Intro to BigQuery
  • Data Lakes and Data Warehouses
  • 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
  • Analyzing Data with BigQuery
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
  • Loading Taxi Data into Cloud SQL
Building a Data Warehouse
  • The modern data warehouse
  • Intro to BigQuery
  • Demo: Query TB+ of data in seconds
  • Getting Started
  • Loading Data
  • Querying Cloud SQL from BigQuery
  • Loading Data into BigQuery
  • Exploring Schemas
  • Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA
  • Schema Design
  • Nested and Repeated Fields
  • Nested and repeated fields in BigQuery
  • Working with JSON and Array data in BigQuery
  • Optimizing with Partitioning and Clustering
  • Partitioned and Clustered Tables in BigQuery
  • Transforming Batch and Streaming Data
Introduction to Building Batch Data Pipelines
  • EL, ELT, ETL
  • Quality considerations
  • How to carry out operations in BigQuery
  • ELT to improve data quality in BigQuery
  • Shortcomings
  • ETL to solve data quality issues
Executing Spark on Cloud Dataproc
  • The Hadoop ecosystem
  • Running Hadoop on Cloud Dataproc
  • GCS instead of HDFS
  • Optimizing Dataproc
  • Running Apache Spark jobs on Cloud Dataproc
Serverless Data Processing with Cloud Dataflow
  • Cloud Dataflow
  • Why customers value Dataflow
  • Dataflow Pipelines
  • A Simple Dataflow Pipeline (Python/Java)
  • MapReduce in Dataflow (Python/Java)
  • Side Inputs (Python/Java)
  • Dataflow Templates
  • Dataflow SQL
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
  • Building Batch Data Pipelines visually with Cloud Data Fusion
  • Components
  • UI Overview
  • Exploring Data using Wrangler
  • Building and executing a pipeline graph in Cloud Data Fusion
  • Orchestrating work between GCP services with Cloud Composer
  • Apache Airflow Environment
  • DAGs and Operators
  • DAGs and Operators
  • Workflow Scheduling
  • Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery
  • Monitoring and Logging
  • An Introduction to Cloud Composer
Introduction to Processing Streaming Data
  • Processing Streaming Data
Serverless Messaging with Cloud Pub/Sub
  • Cloud Pub/Sub
  • Publish Streaming Data into Pub/Sub
Cloud Dataflow Streaming Features
  • Cloud Dataflow Streaming Features
  • Streaming Data Pipelines
High-Throughput BigQuery and Bigtable Streaming Features
  • BigQuery Streaming Features
  • Streaming Analytics and Dashboards
  • Cloud Bigtable
  • Streaming Data Pipelines into Bigtable
Advanced BigQuery Functionality and Performance
  • Analytic Window Functions
  • Using With Clauses
  • GIS Functions
  • Mapping Fastest Growing Zip Codes with BigQuery GeoViz
  • Performance Considerations
  • Optimizing your BigQuery Queries for Performance
  • Creating Date-Partitioned Tables in BigQuery
Introduction to Analytics and AI
  • What is AI?
  • From Ad-hoc Data Analysis to Data-Driven Decisions
  • Options for ML models on GCP
Prebuilt ML model APIs for Unstructured Data
  • Unstructured Data is Hard
  • ML APIs for Enriching Data
  • Using the Natural Language API to Classify Unstructured Text.
Custom Model building with SQL in BigQuery ML
  • BigQuery ML for Quick Model Building
  • Train a model with BigQuery ML to predict NYC taxi fares
  • Supported Models
  • Option 1: Predict Bike Trip Duration with a Regression Model in BQML
  • Option 2: Movie Recommendations in BigQuery ML
Conclusion

Training Materials

All GCP training attendees receive comprehensive courseware.

Software Requirements

Students must have the Google Chrome web browser and Internet access.



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