What is a SQL-based lakehouse architecture in this course?

In this course, a SQL-based lakehouse architecture is a way to organize file-based storage, warehouse tables, and analytics models so they work as one system. The emphasis is on designing that system end to end, including dimensional models, ingestion paths, performance tuning, and consistent metrics.

When would you use a lakehouse architecture?

You would use it when your data lives partly in files and partly in structured tables, but you still want one analytical setup for querying and modeling. In this course, it is treated as a practical approach for combining raw data ingestion, historical tracking, and analytics-ready tables in one design.

How does a lakehouse architecture fit into a broader workflow?

It sits between raw data collection and business analysis, giving you the structure that turns incoming data into dependable analytical tables. The course shows how it connects ingestion, dimensional modeling, historical change handling, and metric standardization into a repeatable workflow.

How is a lakehouse architecture different from a traditional data warehouse?

A traditional data warehouse centers on loading everything into warehouse tables first, while a lakehouse also works with file-based storage through structured querying and external table patterns. This course shows that the lakehouse approach still relies on warehouse design principles such as star schemas, surrogate keys, and performance optimization.

Do you need any prerequisites before learning SQL-based lakehouse architecture?

A working knowledge of SQL and core data warehousing ideas, such as joins, fact tables, and dimension tables, is helpful. Because the course is advanced, it focuses more on architecture decisions, optimization, and historical data management than on beginner database basics.

What tools, platforms, or methods are used in this course?

The course centers on SQL in a modern warehouse and lakehouse setting, with a focus on dimensional modeling and external-table-based ingestion. Performance tuning and historical change handling are also part of the implementation work.

What specific tasks will you practice or complete in this course?

You’ll design dimensional models with the right grain and surrogate keys, then optimize those schemas for analytical use. You’ll also build ingestion paths from file storage, manage historical changes in dimensions, and define consistent business metrics.

Data Modeling and Lakehouse Architecture with SQL

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Data Modeling and Lakehouse Architecture with SQL

Dieser Kurs ist Teil von Spezialisierung „Level Up: Advanced SQL for Data Engineering“

Dozent: Professionals from the Industry

Bei enthalten

Mehr erfahren

13 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Fortgeschritten

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

13 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Fortgeschritten

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Design star and snowflake schemas with dimensional modeling principles, creating fact and dimension tables optimized for analytics.
Implement cost-effective data warehouse architectures with partitioning, clustering, and multi-cluster workload isolation strategies.
Build lakehouse data pipelines that integrate file-based storage with structured querying using external tables and modern formats.
Create semantic metrics layers and slowly changing dimension logic to ensure data consistency across enterprise analytics systems.

Kompetenzen, die Sie erwerben

Kategorie: Diagram Design
Kategorie: Issue Tracking
Kategorie: Data Warehousing
Kategorie: Data Modeling
Kategorie: Data Architecture
Kategorie: Semantic Web
Kategorie: Enterprise Modeling
Kategorie: Business Metrics
Kategorie: SQL
Kategorie: Resource Utilization
Kategorie: Star Schema
Kategorie: Data Pipelines
Kategorie: Data Integrity
Kategorie: Database Theory
Kategorie: Database Architecture and Administration
Kategorie: Dimensionality Reduction
Kategorie: Data Storage
Kategorie: Business Logic
Kategorie: Database Design

Werkzeuge, die Sie lernen werden

Kategorie: Data Lakes

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In diesem Kurs gibt es 13 Module

You will design and implement enterprise-grade data models, from traditional star schemas to modern lakehouse architectures. This comprehensive course equips you with the skills to build cost-effective, scalable data solutions that drive business intelligence and analytics.

You'll gain hands-on experience creating dimensional models with surrogate keys, optimizing database schemas through partitioning and clustering, and implementing slowly changing dimensions for historical data tracking. The course covers advanced topics like semantic metrics layers, multi-cluster warehouse architectures, and open-source table formats for data lakes. What makes this course unique is its end-to-end approach to modern data architecture. You'll work with real-world scenarios, from analyzing storage costs to designing data ingestion pipelines that span from raw files to analytics-ready tables. By completion, you'll confidently architect data solutions that balance performance, cost, and scalability—skills essential for senior data engineering and architecture roles in today's data-driven organizations.

Moduldetails

You will examine existing snowflake schemas to pinpoint performance bottlenecks caused by redundant lookup paths and develop systematic approaches for identifying optimization opportunities.

Das ist alles enthalten

3 Videos2 Lektüren1 Aufgabe

3 VideosInsgesamt 17 Minuten

Why Snowflake Schema Analysis Drives Performance Success4 Minuten
Snowflake Schema Fundamentals for Performance Analysis6 Minuten
Step-by-Step Redundant Lookup Identification Process6 Minuten

2 LektürenInsgesamt 26 Minuten

Schema Analysis Principles for Data Warehouse Optimization8 Minuten
Design Star-Schema Fact and Dimension Tables with Surrogate Keys18 Minuten

1 AufgabeInsgesamt 2 Minuten

Snowflake Schema Redundancy Analysis Knowledge Check2 Minuten

You will construct optimized star-schema dimensional models with proper fact and dimension table structures, implementing surrogate keys and design patterns that maximize query performance for analytical workloads.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

You will develop standardized semantic metrics layers that ensure consistent business logic across analytics platforms, eliminate metric drift, and provide a unified source of truth for enterprise reporting.

Das ist alles enthalten

3 Videos1 Lektüre2 Aufgaben

3 VideosInsgesamt 18 Minuten

Why Semantic Layers Transform Enterprise Analytics4 Minuten
Metrics Standardization Concepts and Implementation Patterns9 Minuten
Implementing Metrics Definitions with Standardized Business Logic5 Minuten

1 LektüreInsgesamt 8 Minuten

Semantic Layer Architecture for Enterprise Analytics8 Minuten

2 AufgabenInsgesamt 15 Minuten

Semantic Layer Concepts and Implementation Validation3 Minuten
Comprehensive Semantic Layer Design and Implementation12 Minuten

You will implement advanced partitioning and clustering techniques using SQL DDL commands to optimize query performance for large-scale datasets.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 8 Minuten

Why Database Performance Hits a Wall at Scale2 Minuten
Partitioning and Clustering Fundamentals for Performance Optimization6 Minuten

1 LektüreInsgesamt 12 Minuten

DDL Syntax and Implementation Patterns for Partitioning and Clustering12 Minuten

2 AufgabenInsgesamt 21 Minuten

Design and Implement a Partitioned Data Warehouse Table18 Minuten
Partitioning and Clustering Strategy Assessment3 Minuten

You will evaluate database normalization levels against query performance requirements to make strategic denormalization decisions for optimizing analytical workloads.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 12 Minuten

Denormalization Strategies for Analytical Workloads6 Minuten
Analyzing Query Performance Impact of Normalization Levels6 Minuten

1 LektüreInsgesamt 12 Minuten

Normalization Forms and Performance Impact Analysis12 Minuten

2 AufgabenInsgesamt 23 Minuten

Develop a Schema Refactoring Proposal with Performance Justification20 Minuten
Normalization vs Performance Trade-off Analysis3 Minuten

You will design and document comprehensive Entity-Relationship diagrams that effectively communicate complex data structures and relationships for enterprise data systems.

Das ist alles enthalten

3 Videos1 Lektüre2 Aufgaben1 Unbewertetes Labor

3 VideosInsgesamt 18 Minuten

When Data Models Become Mission-Critical Communication Tools3 Minuten
Professional ER Diagram Design and Documentation Standards6 Minuten
Building Comprehensive ER Diagrams with Professional Tools9 Minuten

1 LektüreInsgesamt 12 Minuten

ER Diagram Components and Advanced Modeling Techniques12 Minuten

2 AufgabenInsgesamt 18 Minuten

ER Diagram Design Knowledge Check3 Minuten
Comprehensive ER Diagram Design and Documentation Assessment 15 Minuten

1 Unbewertetes LaborInsgesamt 18 Minuten

Enterprise Data Modeling: Creating Comprehensive ER Diagrams for Complex Data Structures18 Minuten

You will build automated SCD Type 2 pipelines using MERGE statements and window functions to preserve historical data integrity in enterprise environments.

Das ist alles enthalten

2 Videos2 Aufgaben

You will conduct comprehensive cost analysis of data lifecycle patterns to develop strategic archiving recommendations that balance storage economics with business value.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 18 Minuten

Cost Trend Analysis Techniques for Data Archiving8 Minuten
Calculating Storage Costs and ROI for Archiving Strategies9 Minuten

1 LektüreInsgesamt 10 Minuten

Data Lifecycle Cost Analysis and Storage Economics10 Minuten

2 AufgabenInsgesamt 21 Minuten

Develop Comprehensive Data Archiving Strategy with Cost-Benefit Analysis18 Minuten
Cost Analysis and Archiving Strategy Knowledge Check3 Minuten

You will design scalable multi-cluster data warehouse architectures that isolate workloads for optimal performance while implementing comprehensive cost control and resource management policies.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 9 Minuten

The Business Case for Multi-Cluster Architecture3 Minuten
Designing Workload Isolation and Resource Management Policies6 Minuten

1 LektüreInsgesamt 10 Minuten

Multi-Cluster Architecture Design Principles and Implementation Patterns10 Minuten

2 AufgabenInsgesamt 18 Minuten

Multi-Cluster Architecture Knowledge Check3 Minuten
Multi-Cluster Architecture Mastery Assessment15 Minuten

You will learn the technical implementation of external table configurations to enable direct querying of file-based datasets in cloud storage.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 9 Minuten

Why External Tables Transform Enterprise Analytics2 Minuten
SQL Syntax and Configuration Parameters for External Tables7 Minuten

1 LektüreInsgesamt 10 Minuten

External Table Architecture and Implementation Patterns10 Minuten

2 AufgabenInsgesamt 21 Minuten

Configure Multi-Format External Tables for Enterprise Data Lake18 Minuten
External Table Configuration Knowledge Check3 Minuten

You will develop analytical frameworks to evaluate and compare the technical capabilities of Delta Lake, Apache Iceberg, and Apache Hudi for specific business requirements.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 13 Minuten

Feature Comparison Matrix: Schema Evolution and Time Travel5 Minuten
Building Technical Capability Comparison Matrices8 Minuten

1 LektüreInsgesamt 10 Minuten

Comprehensive Analysis of Delta Lake, Iceberg, and Hudi Capabilities10 Minuten

2 AufgabenInsgesamt 23 Minuten

Develop Strategic Table Format Recommendation Framework20 Minuten
Table Format Analysis and Selection Knowledge Check3 Minuten

You will architect and implement automated data ingestion pipelines that orchestrate data movement across medallion architecture zones within lakehouse platforms.

Das ist alles enthalten

2 Videos1 Lektüre2 Aufgaben

2 VideosInsgesamt 10 Minuten

The Cost of Manual Data Pipeline Management3 Minuten
COPY INTO Commands and Incremental Loading Strategies7 Minuten

1 LektüreInsgesamt 10 Minuten

Medallion Architecture and Pipeline Orchestration Patterns10 Minuten

2 AufgabenInsgesamt 18 Minuten

Data Ingestion Pipeline Implementation Knowledge Check3 Minuten
Lakehouse Data Pipeline Implementation Assessment15 Minuten

You will design and implement a comprehensive data lakehouse architecture that integrates dimensional modeling, schema optimization, cost management, and multi-format data ingestion. This project synthesizes advanced SQL skills to create a production-ready data engineering solution.