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Modern Data Stack (dbt, Snowflake, Fivetran)
Master modern data engineering with dbt, Snowflake, and Fivetran for scalable analytics workflows.
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Modern Data Stack – Building Scalable Data Pipelines and Analytics Systems
Modern Data Stack (dbt, Snowflake, Fivetran) is an advanced, hands-on course designed to help learners build efficient, automated, and scalable data pipelines for modern analytics and business intelligence.
The modern data stack represents the new standard for cloud-native data engineering — combining data ingestion (Fivetran), data warehousing (Snowflake), and data transformation (dbt) into a seamless, modular workflow. This course provides a complete understanding of how these technologies integrate to create a high-performing, cost-efficient, and maintainable analytics environment.
Through a blend of conceptual learning and guided implementation, you’ll gain end-to-end exposure to modern data architecture — from ingestion and modeling to transformation, orchestration, and analytics delivery. By mastering dbt, Snowflake, and Fivetran, you’ll learn how to move away from legacy ETL toward ELT workflows, empowering teams to transform data directly inside the data warehouse for agility and scalability.
Why Learn Modern Data Stack (dbt, Snowflake, Fivetran)?
Legacy ETL processes are being replaced by modular, flexible, and scalable ELT architectures powered by tools like dbt, Snowflake, and Fivetran. These technologies empower data teams to move faster, ensure data integrity, and support real-time decision-making across the enterprise.
Learning the modern data stack allows you to:
- Simplify data pipeline development and management.
- Leverage the power of cloud data warehousing for performance and scale.
- Automate ingestion and transformation workflows with minimal coding.
- Deliver analytics-ready data faster to stakeholders and BI platforms.
Companies like Netflix, Airbnb, and Spotify rely on the modern data stack for real-time, data-driven decision-making — making these skills essential for today’s data professionals.
What You Will Gain
By the end of this course, you will be able to:
- Design and implement cloud-native ELT data pipelines using Fivetran, Snowflake, and dbt.
- Build scalable and modular data transformation workflows with dbt.
- Understand Snowflake’s architecture, storage, compute, and cost optimization strategies.
- Integrate data from multiple sources using Fivetran connectors.
- Implement data quality testing, version control, and documentation in dbt.
- Deploy automated analytics pipelines and enable data democratization.
You’ll also complete practical projects, including:
- Building a fully automated ELT pipeline using Fivetran + Snowflake + dbt.
- Developing a data model and transformation layer in dbt.
- Implementing a modern analytics workflow integrated with BI tools like Looker or Power BI.
Who This Course Is For
This course is perfect for:
- Data Engineers & Analysts transitioning to cloud-native data stacks.
- BI Developers & Analytics Engineers automating data modeling workflows.
- Database Administrators optimizing data pipelines in the cloud.
- Students & Professionals aspiring to become modern data engineers.
- Organizations & Startups modernizing their data infrastructure.
Whether you’re an experienced data professional or a learner exploring analytics engineering, this course provides the frameworks and practical knowledge needed to excel in modern data operations.
By the end of this course, learners will be able to:
- Understand the architecture and ecosystem of the modern data stack.
- Build ELT workflows integrating Fivetran, dbt, and Snowflake.
- Design data models and transformations following best practices.
- Implement testing, documentation, and CI/CD pipelines for dbt projects.
- Optimize data warehousing performance and cost in Snowflake.
- Deploy scalable, production-grade analytics environments in the cloud.
Course Syllabus
Module 1: Introduction to the Modern Data Stack
Overview of the modern data ecosystem, ELT vs ETL, and data engineering evolution.
Module 2: Understanding ELT Workflows
Concepts of extraction, loading, and in-warehouse transformation using dbt and Snowflake.
Module 3: Fivetran Fundamentals
Data ingestion, connectors, schema mapping, scheduling, and monitoring pipelines.
Module 4: Introduction to Snowflake
Architecture, virtual warehouses, micro-partitions, and query optimization.
Module 5: dbt Basics and Project Setup
dbt installation, project creation, model building, and modular SQL transformations.
Module 6: Data Modeling and Transformation in dbt
Staging, intermediate, and mart layers; Jinja templating and ref() usage.
Module 7: Testing, Documentation, and CI/CD in dbt
Defining data tests, generating docs, and implementing version control and CI/CD pipelines.
Module 8: Integrating Fivetran with Snowflake
Creating pipelines from source systems to Snowflake; schema automation and syncs.
Module 9: Orchestrating End-to-End Data Pipelines
Using dbt Cloud or Airflow for scheduling and managing workflows.
Module 10: Performance Tuning and Cost Optimization
Optimizing queries, caching, warehouse scaling, and Snowflake credits management.
Module 11: Analytics and Business Intelligence Integration
Connecting Snowflake with BI tools (Power BI, Tableau, Looker) for dashboards and insights.
Module 12: Capstone Project – Building a Full Modern Data Pipeline
Design, implement, and deploy a complete pipeline integrating Fivetran, dbt, and Snowflake with automated transformations and visual reporting.
Upon completion, learners will receive a Certificate of Mastery in Modern Data Stack (dbt, Snowflake, Fivetran) from Uplatz.
This certificate validates your ability to design, build, and deploy scalable ELT workflows and modern data architectures that power enterprise analytics solutions.
Mastery of the modern data stack positions you for high-demand roles across industries such as technology, finance, retail, and healthcare. Career paths include:
- Analytics Engineer
- Data Engineer (Cloud/ELT)
- BI Developer
- Data Platform Engineer
- Data Operations Specialist
- Modern Data Stack Consultant
Organizations increasingly seek professionals skilled in dbt, Fivetran, and Snowflake to lead their data modernization journeys, making this expertise both current and future-proof.
- What is the Modern Data Stack?
It’s a collection of cloud-based tools (like dbt, Fivetran, and Snowflake) that together enable scalable, automated, and modular data processing pipelines. - What’s the difference between ETL and ELT?
ETL transforms data before loading, while ELT loads raw data into the warehouse and transforms it inside using tools like dbt. - What is the role of dbt in the Modern Data Stack?
dbt handles data transformation, testing, and documentation directly inside the data warehouse. - What is Fivetran used for?
Fivetran automates data ingestion by extracting data from sources (like CRMs or SaaS apps) and loading it into a destination like Snowflake. - What are Snowflake’s key advantages?
Elastic scalability, separation of compute and storage, concurrency handling, and zero-copy cloning. - How does dbt improve data quality?
Through automated tests, modular SQL models, and version-controlled pipelines ensuring data reliability and consistency. - What’s the difference between dbt Core and dbt Cloud?
dbt Core is open-source and runs locally, while dbt Cloud offers managed scheduling, version control, and collaboration features. - How do you optimize Snowflake performance?
By adjusting warehouse size, using result caching, clustering, and query profiling. - What challenges do teams face in implementing a Modern Data Stack?
Managing data lineage, maintaining data quality, cost control, and ensuring integration consistency. - What are real-world use cases of Modern Data Stack adoption?
Business intelligence reporting, real-time analytics, marketing data pipelines, and data-driven product insights.