Data Engineering on Google Cloud Platform

The Data Engineering on Google Cloud Platform 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

The Data Engineering on Google Cloud Platform 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, visualizing query results and creating reports

• 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 Python
• Familiarity with Machine Learning and/or statistics

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.

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.

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.

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.

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.

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.

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.

Introduction to Processing Streaming Data

• Processing Streaming Data.

Serverless Messaging with Cloud Pub/Sub

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

Cloud Dataflow Streaming Features

• Cloud Dataflow Streaming Features.
• Lab: Streaming Data Pipelines.

High-Throughput BigQuery and Bigtable Streaming Features

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

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.

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.
• Lab: Using the Natural Language API to Classify Unstructured Text.

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.

Production ML Pipelines with Kubeflow

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

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.

Custom Model building with Cloud AutoML

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