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.

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

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

Ønsker du å samle flere ansatte til et bedriftsinternt kurs?

Finner du ikke det helt optimale kurset eller kombinasjonen av kurs? Da ordner vi det - sammen. Vi kan tilrettelegge kurs slik at de inneholder akkurat det dere har behov for. Vi kan sette opp et helt nytt kurs, eller tilpasse eksisterende kurs og materiell. Flere medarbeidere kan selvfølgelig også samles til et eget felles kurs, for maksimal effektivitet. Ta kontakt med meg for et forslag til gjennomføring og et tilbud basert på deres behov.

Henrik Buzzi
Produktansvarlig