Cloud

Data Engineering on Google Cloud

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.

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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Henrik Buzzi

Henrik Buzzi

Produktansvarlig

henrik.buzzi@bouvet.no

+47 93808080