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Data Engineering on Google Cloud

4.6 out of 5 rating Last updated 13/12/2024   English

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Duration

4 Days

24 CPD hours

Overview

Design and build data processing systems on 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.
Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML.
Enable instant insights from streaming data

Description

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

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
  • What's 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.
Additional course details:

Nexus Humans Data Engineering on Google Cloud training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Data Engineering on Google Cloud course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

FAQ for the Data Engineering on Google Cloud Course

Available Delivery Options for the Data Engineering on Google Cloud training.
  • Live Instructor Led Classroom Online (Live Online)
  • Traditional Instructor Led Classroom (TILT/ILT)
  • Delivery at your offices in London or anywhere in the UK
  • Private dedicated course as works for your staff.
How many CPD hours does the Data Engineering on Google Cloud training provide?

The 4 day. Data Engineering on Google Cloud training course give you up to 24 CPD hours/structured learning hours. If you need a letter or certificate in a particular format for your association, organisation or professional body please just ask.

What is the correct audience for the Data Engineering on Google Cloud training?

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

Do you provide training for the Data Engineering on Google Cloud.

Yes we provide corporate training, dedicated training and closed classes for the Data Engineering on Google Cloud. This can take place anywhere in Ireland including, Dublin, Cork, Galway, Northern Ireland or live online allowing you to have your teams from across Ireland or further afield to attend a single training event saving travel and delivery expenses.

What is the duration of the Data Engineering on Google Cloud program.

The Data Engineering on Google Cloud training takes place over 4 day(s), with each day lasting approximately 8 hours including small and lunch breaks to ensure that the delegates get the most out of the day.

Why are Nexus Human the best provider for the Data Engineering on Google Cloud?
Nexus Human are recognised as one of the best training companies as they and their trainers have won and hold many awards and titles including having previously won the Small Firms Best Trainer award, national training partner of the year for Ireland on multiple occasions, having trainers in the global top 30 instructor awards in 2012, 2019 and 2021. Nexus Human has also been nominated for the Tech Excellence awards multiple times. Learning Performance institute (LPI) external training provider sponsor 2024.
Is there a discount code for the Data Engineering on Google Cloud training.

Yes, the discount code PENPAL5 is currently available for the Data Engineering on Google Cloud training. Other discount codes may also be available but only one discount code or special offer can be used for each booking. This discount code is available for companies and individuals.

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Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.

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