Data Engineering on Google Cloud

Varighet: 4 dager, kl 09:00 -17:00

Pris: 32000

Kurskategori: Cloud

Underkategori: Google Cloud

Kursdatoer er ikke helt avklart ennå, men kontakt [email protected] for påmelding!

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.


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


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 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
-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

Kursdatoer er ikke helt avklart ennå, men kontakt [email protected] for påmelding!

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Henrik Buzzi