Modern organisations depend on scalable data platforms capable of collecting, transforming, storing, and analysing vast amounts of information from multiple sources. Google Cloud provides a comprehensive ecosystem of managed services that enable data engineers to build reliable and high-performance data solutions.

Data Engineering on Google Cloud is a hands-on training programme designed to help participants design, implement, and manage end-to-end data processing solutions using Google Cloud technologies. Through instructor-led demonstrations and practical labs, learners gain experience with data lakes, data warehouses, ETL and ELT architectures, batch processing, streaming analytics, and pipeline automation.

The course covers Google's core data engineering services and prepares participants to develop production-ready data pipelines that support analytics, reporting, and business intelligence initiatives.

Google Cloud Services Covered

BigQuery
Bigtable
Cloud Storage
Cloud SQL
Cloud Spanner
Dataproc
Dataflow
Cloud Data Fusion
Cloud Composer
Pub/Sub

For corporate groups, we can organize this training as an on-site private group.

Prerequisites

Participants are recommended to have:

Experience using Google Cloud Console and Cloud Shell
Working knowledge of SQL
Familiarity with ETL processes and data modelling concepts
Programming experience in Python or another common language

Who Should Attend

This programme is intended for:

Data Engineers
Database Administrators
System Administrators
Cloud Engineers
Analytics Engineers
Data Platform Specialists
Professionals building data solutions on Google Cloud

What You Will Learn

Upon successful completion of this course, participants will be able to:

Design scalable data processing architectures on Google Cloud.
Develop batch and streaming data pipelines using managed cloud services.
Build autoscaling pipelines with Dataflow.
Analyse massive datasets efficiently using BigQuery.
Process structured and unstructured data with Apache Spark on Dataproc.
Implement real-time analytics solutions using streaming technologies.
Design modern data lakes and cloud data warehouses.
Select appropriate Google Cloud services for storage, processing, orchestration, and governance.

Training Outline

Module 1 – Introduction to Data Engineering

Responsibilities of a Data Engineer
Data sources and synchronisation
Data formats
Google Cloud storage services
Metadata management
Sharing datasets through Analytics Hub

Module 2 – Data Replication and Migration

Replication architectures
gcloud CLI
Data migration strategies
Datastream

Module 3 – Extract and Load Pipelines

EL architecture
bq command-line tool
BigQuery Data Transfer Service
BigLake

Module 4 – ELT Pipelines

Extract, Load and Transform architecture
SQL scripting in BigQuery
Scheduling jobs
Dataform

Module 5 – ETL Pipelines

ETL architecture
Visual ETL tools
Batch processing with Dataproc
Streaming processing options
Bigtable integration

Module 6 – Pipeline Automation

Automation patterns
Cloud Scheduler
Workflows
Cloud Composer
Cloud Run Functions
Eventarc

Module 7 – Modern Data Engineering Concepts

Data engineering fundamentals
Common implementation challenges
BigQuery overview
Data lakes and warehouses
Transactional databases versus analytical platforms
Data governance
Access management
Production-ready pipelines
Google Cloud customer case studies

Module 8 – Building a Data Lake

Data lake architecture
Cloud Storage implementation
Secure storage
Managing multiple data formats
Cloud SQL for transactional workloads

Module 9 – Building a Data Warehouse

Modern warehouse architecture
Working with BigQuery
Loading datasets
Schema design
Nested and repeated fields
Partitioning
Clustering optimisation

Module 10 – Batch Data Processing

EL, ELT and ETL approaches
Data quality management
BigQuery execution methods
Performance considerations
Data quality improvements

Module 11 – Apache Spark with Dataproc

Hadoop ecosystem
Running Spark workloads
Cloud Storage integration
Dataproc optimisation

Module 12 – Serverless Data Processing

Dataflow fundamentals
Pipeline development
GroupByKey
Combine
Side inputs
Windowing
Dataflow templates

Module 13 – Pipeline Orchestration

Cloud Data Fusion
Pipeline components
Wrangler
Cloud Composer
Apache Airflow
DAG management
Workflow scheduling
Monitoring and logging

Module 14 – Messaging with Pub/Sub

Pub/Sub architecture
Push and Pull subscriptions
Publishing messages

Module 15 – Streaming Data Processing

Streaming architectures
Windowing strategies
Real-time analytics

Module 16 – BigQuery & Bigtable Streaming

Streaming into BigQuery
Bigtable optimisation
High-throughput data ingestion

Module 17 – Advanced BigQuery

Analytic window functions
GIS capabilities
Query and performance optimisation

Why Choose Us

Leading UK-based global training provider since 1995.

Experience Data Engineering on Google Cloud in Sweden through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

Delivery Method: Online Instructor-Led
Participation Model: Public / Private (In-house)
Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
Worldwide Access: Join our live virtual classrooms from Sweden or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience Data Engineering on Google Cloud through face-to-face Classroom Based training in Sweden. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

Delivery Method: Classroom Based
Participation Model: Public / Private (In-house)
Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Data Engineering on Google Cloud in Sweden as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

Delivery Method: Onsite
Participation Model: Private (In-house)
Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
Worldwide Onsite Delivery: Arrange the training in Sweden or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.

Data Engineering on Google Cloud Training in Sweden

Google Cloud Services Covered

Prerequisites

Who Should Attend

What You Will Learn

Training Outline

Module 1 – Introduction to Data Engineering

Module 2 – Data Replication and Migration

Module 3 – Extract and Load Pipelines

Module 4 – ELT Pipelines

Module 5 – ETL Pipelines

Module 6 – Pipeline Automation

Module 7 – Modern Data Engineering Concepts

Module 8 – Building a Data Lake

Module 9 – Building a Data Warehouse

Module 10 – Batch Data Processing

Module 11 – Apache Spark with Dataproc

Module 12 – Serverless Data Processing

Module 13 – Pipeline Orchestration

Module 14 – Messaging with Pub/Sub

Module 15 – Streaming Data Processing

Module 16 – BigQuery & Bigtable Streaming

Module 17 – Advanced BigQuery

Why Choose Us

Data Engineering on Google Cloud Training Course in Sweden Schedule

Blog posts related to Data Engineering on Google Cloud Training Course in Sweden

Data Engineering on Google Cloud Training in Sweden

Google Cloud Services Covered

Prerequisites

Who Should Attend

What You Will Learn

Training Outline

Module 1 – Introduction to Data Engineering

Module 2 – Data Replication and Migration

Module 3 – Extract and Load Pipelines

Module 4 – ELT Pipelines

Module 5 – ETL Pipelines

Module 6 – Pipeline Automation

Module 7 – Modern Data Engineering Concepts

Module 8 – Building a Data Lake

Module 9 – Building a Data Warehouse

Module 10 – Batch Data Processing

Module 11 – Apache Spark with Dataproc

Module 12 – Serverless Data Processing

Module 13 – Pipeline Orchestration

Module 14 – Messaging with Pub/Sub

Module 15 – Streaming Data Processing

Module 16 – BigQuery & Bigtable Streaming

Module 17 – Advanced BigQuery

Why Choose Us

Online Instructor-Led Training

Classroom Based Training

Onsite Training

Data Engineering on Google Cloud Training Course in Sweden Schedule

Blog posts related to Data Engineering on Google Cloud Training Course in Sweden

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