Available courses

History of Data

The History of Data

FREE Intro | ~2 hours

From punched cards to Lakehouses and AI-ready data platforms: understand the evolution of data and why the future is starting now.

What you'll gain (free entry point):

  • ✅ Key milestones in data technology (1940–2025)
  • ✅ Why relational databases, Hadoop, cloud, and lakehouses emerged
  • ✅ The shift from batch → real-time → AI-driven data
  • ✅ Lessons from the past you can apply today
  • ✅ Context for modern architectures (Lakehouse, Semantic, etc.)

Ideal as a first step for anyone serious about data engineering or architecture.

Outline (~2 hours total):

  • The early days: punched cards & mainframes
  • The relational revolution & SQL
  • Big Data & the Hadoop era
  • Cloud, data lakes & warehouses
  • The rise of Lakehouse, AI & semantic data
  • Summary & what it means for you

Start completely free – no credit card, no catch. Discover the history behind your future work.

Skill Level: Beginner

Course on Mastering dbt - introduction and core concepts

Course on Mastering dbt - introduction and core concepts

Section 1: Introduction to dbt [1 hour]

  • What is dbt: Overview of dbt and its role in modern data transformation workflows.

  • Importance in E-Commerce: How dbt can optimize data-driven decision-making in e-commerce sectors.

  • Key Features: Understand dbt's fundamental features, including modular SQL development, testing, and documentation.

  • Visualizing Data Lineage: Explore dbt's ability to map the entire data lifecycle from raw data to analytics.

Section 2: ELT vs. ETL [1 hour]

  • ETL Overview: Brief overview of the traditional Extract, Transform, Load process.

  • ELT Explained: Understanding Extract, Load, Transform and its advantages in cloud environments.

  • Transition to ELT: Why more businesses, particularly in e-commerce, choose ELT over ETL.

  • Case Study Comparison: A look at how ELT with tools like dbt optimizes performance.

Section 3: dbt and Medallion Architecture [1 hour]

  • Medallion Architecture Explained: Overview and how it structures data flow.

  • Role of dbt: Examine dbt's integration and contribution to the medallion architecture.

  • Benefits in E-Commerce: How using dbt within this architecture improves data quality and accessibility.

  • Real-life Applications: Analysis of e-commerce scenarios utilizing dbt and medallion architecture.

Section 4: Installation and Setup Guide [1 hour]

  • Prerequisites Check: Reviewing basic SQL, git, and Python skills necessary to proceed.

  • Tool Setup: Choosing between dbt Cloud and Core with pros/cons.

  • Docker Installation: Step-by-step guide to install a Postgres Docker container for the Jaffle Shop sample project.

  • Profile and Project Configurations: Configuring profile.yml and dbt_project.yml for successful dbt project setup.

Section 5: Final Review

  • Review of Key Concepts: Summarize dbt's role, ELT vs. ETL, and medallion architecture insights.

  • Future Learning Directions: Suggestions for advancing into advanced dbt functionalities and data engineering skills.

Skill Level: Beginner

Course on dbt semantic layer

Unlock the Power of Consistent, Actionable Insights with dbt Semantic Layer Master dbt’s Semantic Layer Using a Practical School Database Example

Are you a data engineer, analyst, or BI professional tired of inconsistent metrics, duplicated logic across tools, and endless back-and-forth with stakeholders?

Discover how the dbt Semantic Layer revolutionizes data engineering by centralizing your business logic, ensuring every team works from the same source of truth. This hands-on course uses a relatable school database scenario — perfect for educators, EdTech professionals, or anyone wanting an intuitive, real-world context to learn advanced concepts.

Why Enroll in This Course?

  • Build Trustworthy Data Products: Learn to define entities, metrics, and dimensions once in dbt — and reuse them everywhere with perfect consistency.
  • Hands-on & Immediately Applicable: Work directly with DuckDB and integrate seamlessly with Google Looker for stunning visualizations.
  • Practical School-Focused Examples: From student enrollments and attendance rates to exam performance metrics — see exactly how semantic layers drive educational insights.
  • Efficient & Modern Workflow: Move beyond fragmented queries to governed, self-serve analytics that scale.

Course Structure (5 Hours Total)

Section 1: An Introduction to Semantics in Data Engineering (1 hour) Understand what a semantic layer is, why it matters, its evolution in the industry, and how it delivers consistency, accessibility, and faster insights — using school data as the perfect illustration.

Section 2: Setting up the dbt Semantic Layer for a School Database (1 hour) Get started with dbt fundamentals, configure the semantic layer with DuckDB, connect to Google Looker, and troubleshoot common setup challenges.

Section 3: Diving into Entities and Metrics in a School Context (1 hour) Define and manage entities (students, classes, courses) and build powerful metrics (graduation rates, average scores, retention) tailored to educational environments.

Section 4: Querying School Data with the dbt Semantic Layer (1 hour) Master querying via dbt CLI and Google Looker. Compare approaches, apply best practices, and extract meaningful insights from your school dataset.

Section 5: Final Review and Path Forward in Data Engineering Consolidate your learning, review integrations and querying techniques, and get clear next steps for advancing your skills in educational (or any domain) data engineering.

Who Is This Course For?

  • Data engineers and analysts implementing or expanding dbt projects
  • BI developers and dashboard creators seeking metric governance
  • Educators, EdTech data teams, or professionals who want practical, relatable examples
  • Anyone ready to move from raw data pipelines to governed, business-ready analytics

By the end of this course, you’ll confidently design, implement, and query a production-grade semantic layer — turning complex school (or business) data into reliable, reusable insights that drive real decisions.

Skill Level: Beginner

Lakehouse Architectures for Data Engineers

Lakehouse Architectures for Data Engineers

Advanced | 8–12 hours

Build modern, scalable data platforms that combine a data lake and data warehouse into one. Hands-on with the best patterns and tools used today by leading organizations.

What you'll learn (hands-on + assessments):

  • ✅ Medallion architecture (bronze/silver/gold) in practice
  • ✅ Delta Lake, Apache Iceberg & Hudi – comparison & implementation
  • ✅ Unity Catalog, catalog management and governance
  • ✅ Real-time & batch processing on a single platform
  • ✅ Performance tuning, schema evolution & time travel
  • ✅ Migration strategies from classic warehouses/lakes

Perfect for data engineers and architects who refuse to build outdated platforms.

Outline (8–12 hours total):

  • Intro to Lakehouse concept & benefits
  • Medallion architecture in detail
  • Delta Lake & Iceberg deep-dive
  • Unity Catalog & governance
  • Performance, security & best practices
  • Real-world assessments & case studies
  • Final review & next steps

Ready to future-proof your data platform? Start now with the Assessment Edition.

Skill Level: Beginner

Metadata & Meaning: Distributed Semantic Ontologies and Metadata Management at Scale

Semantic Ontologies & Metadata at Scale

Advanced | ~16–20 hours

Build AI-ready knowledge graphs and semantic layers that make your metadata scalable, validatable, and future-proof.

What you'll learn (hands-on):

  • ✅ RDF, OWL & SPARQL – from basics to advanced queries
  • ✅ Protégé + owlready2 for ontology design & Python integration
  • ✅ SHACL for data validation & DCAT/OpenLineage metadata standards
  • ✅ Semantic Governance: centralized vs federated models
  • ✅ HybridRAG, Knowledge Mesh & semantic lineage in AI pipelines
  • ✅ Enterprise-grade data contracts & ontologies

Perfect for data engineers, architects & AI specialists who want to tame complex metadata and make platforms AI-proof.

Outline (~16–20 hours total):

  • Intro & RDF/OWL/SPARQL basics
  • Metadata standards (OpenLineage, DCAT, SHACL)
  • Ontology building & querying (Protégé, owlready2)
  • Advanced governance & HybridRAG
  • Semantic layers for AI pipelines & knowledge mesh
  • Data contracts + SHACL in practice
  • Final review & next steps

Ready to make your data semantic? Start now and unlock real-world applications.

Skill Level: Beginner