What is the Medallion Architecture covered in this course?

Medallion Architecture is a layered data design pattern using Bronze (raw), Silver (cleaned), and Gold (analytics-ready) layers. The course includes hands-on pipeline building across all three layers using Delta Lake on Databricks.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Lakehouse Architecture and Delta Lake with Databricks

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Lakehouse Architecture and Delta Lake with Databricks

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

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

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

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

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Design and implement Lakehouse architectures using Databricks and Delta Lake to replace legacy data platforms
Build end-to-end data pipelines using Medallion Architecture (Bronze, Silver, Gold) with incremental processing and Change Data Capture
Apply Delta Lake performance optimization techniques—including data skipping, file compaction, and Liquid Clustering—to support BI and ML workloads
Manage production-grade data reliability through ACID transactions, time travel, schema enforcement, and concurrency control

Kompetenzen, die Sie erwerben

Kategorie: Data Storage Technologies
Kategorie: Data Integration
Kategorie: Data Infrastructure
Kategorie: Scalability
Kategorie: Real Time Data
Kategorie: SQL
Kategorie: Big Data
Kategorie: Transaction Processing
Kategorie: Data Architecture
Kategorie: Data Warehousing
Kategorie: Performance Tuning
Kategorie: Data Pipelines
Kategorie: Data Processing
Kategorie: Data Quality

Werkzeuge, die Sie lernen werden

Kategorie: Databricks
Kategorie: Data Lakes
Kategorie: Apache Spark

Wichtige Details

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Kürzlich aktualisiert!

Februar 2026

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16 Zuweisungen¹

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In diesem Kurs gibt es 4 Module

Design and implement production ready Lakehouse architectures using Delta Lake and Databricks. By the end of this course, you will be able to build multi layer Medallion pipelines including Bronze, Silver, and Gold layers, manage ACID transactions, enforce and evolve schemas, implement Change Data Capture, and optimize Delta tables for performance using data skipping, compaction, and Liquid Clustering. You will also learn to unify batch and streaming workloads while ensuring reliability, scalability, and recoverability in enterprise environments.

This course stands out by going beyond Delta Lake syntax and focusing on end to end Lakehouse engineering, from architectural design patterns to production optimization and concurrency control. Through structured modules and hands on implementation, you will gain practical experience designing scalable data platforms that support both BI analytics and machine learning workloads. If you are a data engineer, analytics engineer, or platform architect looking to modernize legacy data warehouses or data lakes, this course provides the applied skills required to build efficient, cost effective, and future ready data infrastructure on Databricks. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.

This module introduces the evolution of modern data platforms, from traditional warehouses and data lakes to the unified Lakehouse architecture. Learners explore foundational concepts of Databricks, Apache Spark, and Delta Lake that enable scalable, reliable, and governed data processing.

Das ist alles enthalten

15 Videos5 Lektüren4 Aufgaben

15 VideosInsgesamt 79 Minuten

Course Introduction5 Minuten
Warehouses to Lakehouse Data Architecture6 Minuten
Limitations of Traditional Data Warehouses and Data Lakes6 Minuten
Introduction to Databricks5 Minuten
Demonstration: Exploring Databricks Architecture6 Minuten
What is Lakehouse Architecture and Why It Matters6 Minuten
Principles of Lakehouse Architecture5 Minuten
Data Lake vs. Data Warehouse vs. Lakehouse5 Minuten
Demonstration: Databricks Workspace and Lakehouse Components Overview4 Minuten
Introduction to Delta Lake on Databricks6 Minuten
Delta Lake Architecture and Core Components6 Minuten
ACID Transactions in Delta Lake6 Minuten
Demonstration: Apache Spark Overview6 Minuten
Demonstration: Creating and Querying First Delta Table on Databricks4 Minuten
Demonstration: Understanding the Delta Transaction Log5 Minuten

5 LektürenInsgesamt 75 Minuten

Course Syllabus15 Minuten
Evolution of Analytics Workloads and Data Consumption Patterns15 Minuten
Organizational Transition from Data Lakes to Lakehouse Architectures15 Minuten
Open Table Formats: Delta Lake, Iceberg, and Hudi15 Minuten
Module Summary: Modern Data Architecture Fundamentals15 Minuten

4 AufgabenInsgesamt 33 Minuten

Practice Knowledge Check: Modern Data Architecture Fundamentals6 Minuten
Practice Knowledge Check: Lakehouse Architecture Fundamentals6 Minuten
Practice Knowledge Check: Apache Spark, Delta Lake, and Environment Setup6 Minuten
Knowledge Check: Modern Data Architecture Fundamentals15 Minuten

This module focuses on the core operational capabilities of Delta Lake, including storage architecture, metadata management, transactional processing, and schema control. Learners gain hands-on experience with CRUD operations, incremental data pipelines, time travel, and streaming to build reliable, production-ready data workflows.

Das ist alles enthalten

12 Videos4 Lektüren4 Aufgaben

12 VideosInsgesamt 55 Minuten

Introduction to Metadata in Delta Lake5 Minuten
Schema Enforcement vs. Schema Evolution6 Minuten
Demonstration: Inspecting Delta Table Versions and History4 Minuten
Demonstration: Enforcing and Evolving Schema in Delta Tables4 Minuten
Transactional Data Operations in Delta Lake7 Minuten
Demonstration: Creating Managed and External Delta Tables7 Minuten
Demonstration: Performing CRUD Operations on Delta Tables3 Minuten
Demonstration: Implementing MERGE for Incremental Loads3 Minuten
Time Travel and Versioned Data Access5 Minuten
Streaming and Batch Unification in Delta Lake5 Minuten
Demonstration: Data Auditing and Recovery Using Time Travel3 Minuten
Demonstration: Streaming Reads and Writes with Delta Lake3 Minuten

4 LektürenInsgesamt 60 Minuten

Reading: Practical Usage of Delta Lake in Production Environments15 Minuten
Designing Incremental Data Pipelines for Large-Scale Systems15 Minuten
Data Lineage, Auditing, and Observability15 Minuten
Module Summary: Core Delta Lake Concepts and Operations15 Minuten

4 AufgabenInsgesamt 33 Minuten

Practice Assignment: Delta Lake Storage, Metadata, and Schema Management6 Minuten
Practice Knowlede Check: Delta Table Types and Data Modification6 Minuten
Time Travel and Streaming with Delta Lake6 Minuten
Knowledge Check: Core Delta Lake Concepts and Operations15 Minuten

This module focuses on designing scalable Lakehouse architectures using Medallion patterns and optimizing Delta Lake for performance and cost efficiency. Learners build multi-layer data pipelines and apply advanced optimization techniques to support BI and machine learning workloads.

Das ist alles enthalten

13 Videos5 Lektüren4 Aufgaben

13 VideosInsgesamt 70 Minuten

Introduction to Medallion Architecture in the Lakehouse7 Minuten
Layered Data Refinement Using Medallion Architecture6 Minuten
Lakehouse Pipeline Architecture Design5 Minuten
Demonstration: Building Bronze to Silver Pipelines using Medallion Architecture6 Minuten
Demonstration: Transforming Data into Gold Tables5 Minuten
Data Skipping, Statistics, and File Pruning6 Minuten
Delta Table Performance Optimization7 Minuten
Demonstration: File Compaction and Storage Optimization using OPTIMIZE4 Minuten
Liquid Clustering Overview5 Minuten
Demonstration: Validating the impact of Delta Lake optimizations4 Minuten
Delta Lake Interoperability with BI Tools6 Minuten
Using Delta Lake for Machine Learning Workloads5 Minuten
Demonstration: Diagnosing the Small File Problem4 Minuten

5 LektürenInsgesamt 75 Minuten

Pipeline Orchestration and Dependency Management15 Minuten
Lakeflow Connect on Databricks15 Minuten
Balancing Cost, Latency, and Scalability15 Minuten
Choosing Storage Layouts for Analytics, BI, and ML15 Minuten
Module Summary: Lakehouse Architecture and Performance Optimization15 Minuten

4 AufgabenInsgesamt 33 Minuten

Practice Assignment: Lakehouse Design Patterns6 Minuten
Practice Knowledge Check: Delta Lake Performance Optimization Techniques6 Minuten
Practice Knowledge Check: Storage Efficiency and Interoperability6 Minuten
Knowledge Check: Lakehouse Architecture and Performance Optimization15 Minuten

Das ist alles enthalten

9 Videos3 Lektüren4 Aufgaben

9 VideosInsgesamt 50 Minuten

Concurrency Control and Optimistic Transactions7 Minuten
Isolation Levels in Delta Lake6 Minuten
Data Quality, Constraints, and Expectations6 Minuten
Demonstration: Simulating Concurrent Writes in Delta Lake7 Minuten
Demonstration: Applying Constraints and Validating Data in Delta Lake5 Minuten
Introduction to Change Data Capture6 Minuten
Demonstration: Using Delta Change Data Feed in Delta Lake5 Minuten
Demonstration: Building Incremental CDC Pipelines using Delta Lake7 Minuten
Course Summary3 Minuten

3 LektürenInsgesamt 60 Minuten

Failure Modes and Recovery in Distributed Data Systems15 Minuten
Event-Driven Architectures and Incremental Processing15 Minuten
Practice Project: Building an End-to-End Lakehouse on Databricks with Delta Lake30 Minuten

4 AufgabenInsgesamt 72 Minuten

Practice Knowledge Check: Advanced Delta Lake Internals and Reliability6 Minuten
Practice Knowledge Check: Change Data Capture with Delta Lake6 Minuten
End Course Knowledge Check: Lakehouse Architecture and Delta Lake with Databricks30 Minuten
Designing a Production-Ready Enterprise Lakehouse Modernization Strategy30 Minuten

Dozent

Edureka

185 Kurse175.899 Lernende

von

Edureka

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Häufig gestellte Fragen

Delta Lake is an open-source storage layer that brings ACID transactions, schema enforcement, and time travel to data lakes. This course teaches you to use Delta Lake natively on Databricks to build reliable, scalable data pipelines.

No. The course starts from the Databricks environment and workspace setup. Basic SQL and Python knowledge is recommended, but no prior Databricks or Delta Lake experience is required.

A Lakehouse combines the low-cost storage of a data lake with the reliability and performance of a data warehouse. You'll learn how Databricks implements this unified architecture using Delta Lake as its core.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

Weitere Fragen

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Finanzielle Unterstützung verfügbar,

¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.