Will I get hands-on experience with Delta Lake?

Yes. The course includes demonstrations on creating Delta tables, performing CRUD operations, using MERGE INTO, tracking transaction logs, and recovering data with Time Travel.

What PySpark skills are covered?

You will learn filtering, aggregations, joins, duplicate handling, null processing, text transformations, and working with complex data types such as arrays, structs, and maps.

Will I learn how to optimize Delta tables?

Yes. The course covers performance optimization techniques including OPTIMIZE, VACUUM, Z-Ordering, and other best practices for efficient querying.

Does the course include building a complete data pipeline?

Yes. You will build an end-to-end Medallion pipeline by ingesting raw data, cleaning and validating it, and creating business-ready datasets.

Will I learn streaming data processing?

Yes. The course introduces Structured Streaming, Delta Change Data Feed, and techniques for incremental data processing and reliable pipelines.

How does this course prepare me for AI and Machine Learning?

The course demonstrates how high-quality, optimized data pipelines and Medallion Architecture provide clean, reliable datasets for AI and ML applications.

What tools and technologies are covered?

The course focuses on Databricks, Delta Lake, Apache Spark, PySpark, Structured Streaming, Unity Catalog concepts, and Medallion Architecture.

Is this course part of a Specialization?

Yes. It is the second course in the Data Engineering for AI and ML Pipelines Specialization. You can also take it on its own.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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.

Delta Lake and Medallion Architecture for AI and ML

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Delta Lake and Medallion Architecture for AI and ML

Dieser Kurs ist Teil von Spezialisierung „Data Engineering for AI and ML Pipelines“

Dozent: Edureka

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4 Module

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

Stufe Mittel

Empfohlene Erfahrung

7 Stunden zu vervollständigen

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

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Implement Delta Lake features to build reliable, versioned, and high-performance data pipelines.
Apply PySpark transformations and Medallion Architecture to prepare AI-ready datasets.
Optimize Delta tables and enforce data quality for reliable pipeline performance.
Design Medallion Architecture pipelines using Bronze, Silver, and Gold data layers.

Kompetenzen, die Sie erwerben

Kategorie: Data Architecture
Kategorie: Data Cleansing
Kategorie: Data Collection
Kategorie: Data Processing
Kategorie: MLOps (Machine Learning Operations)
Kategorie: SQL
Kategorie: Extract, Transform, Load
Kategorie: Data Maintenance
Kategorie: Data Transformation
Kategorie: Data Pipelines
Kategorie: Data Validation
Kategorie: Data Entry
Kategorie: Data Infrastructure
Kategorie: Data Management
Kategorie: Data Manipulation

Werkzeuge, die Sie lernen werden

Kategorie: Databricks
Kategorie: Data Lakes
Kategorie: Apache Spark
Kategorie: Python Programming
Kategorie: PySpark

Wichtige Details

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Juli 2026

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Data Engineering for AI and ML Pipelines“

Wenn Sie sich für diesen Kurs anmelden, werden Sie auch für diese Spezialisierung angemeldet.

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In diesem Kurs gibt es 4 Module

This course expands your data engineering skills by focusing on Delta Lake and Medallion Architecture for building dependable, scalable, and analytics-ready data platforms. You will learn how modern pipelines maintain data quality, support version control, and prepare trusted datasets for AI/ML workloads.

You will start with Delta Lake fundamentals, including ACID transactions, schema enforcement, schema evolution, transaction logs, and Time Travel. Through practical demonstrations, you will create Delta tables, manage updates and deletes, apply MERGE operations, and restore earlier versions of data when needed. You will then apply PySpark transformation techniques to clean, reshape, join, and validate datasets. You will also explore performance-focused practices such as OPTIMIZE, VACUUM, and Z-Ordering to make Delta tables more efficient for large-scale processing. Next, you will design Medallion Architecture pipelines using Bronze, Silver, and Gold layers. This helps convert raw data into clean, validated, and business-ready datasets for reporting, analytics, and machine learning. The course also introduces structured streaming, Change Data Feed, and Delta constraints for improving pipeline reliability. By the end of this course, you will be able to: - Use Delta Lake for reliable and versioned data management. - Transform and validate datasets using PySpark. - Optimise Delta tables for performance and storage efficiency. - Build Bronze, Silver, and Gold data layers. - Develop dependable batch and streaming pipelines for AI/ML use cases. Designed for data engineers, analytics engineers, software developers, and AI/ML professionals, this course prepares you to build modern data pipelines that are reliable, scalable, and ready for production workloads.

Master the fundamentals of Delta Lake and learn how it enables reliable, scalable, and high-performance data engineering on Databricks. This module covers ACID transactions, schema enforcement and evolution, transaction logs, and versioned data management. Through hands-on exercises, you'll create and manage Delta tables, perform CRUD and MERGE operations, and use Time Travel to track and recover data changes, building the skills needed to develop robust and trustworthy data pipelines.

Das ist alles enthalten

11 Videos3 Lektüren2 Aufgaben

11 VideosInsgesamt 63 Minuten

Specialization Introduction6 Minuten
Course Introduction4 Minuten
Introduction to Delta Lake on Databricks6 Minuten
ACID Transactions in Delta Lake 6 Minuten
Creating and Exploring Delta Tables6 Minuten
Tracking Changes with the Delta Transaction Log6 Minuten
Schema Enforcement and Schema Evolution in Delta Lake6 Minuten
Performing CRUD Operations on Delta Tables5 Minuten
Managing Inserts and Updates with MERGE INTO6 Minuten
Time Travel and Versioned Data Access4 Minuten
Using Delta Lake Time Travel for Data Recovery6 Minuten

3 LektürenInsgesamt 30 Minuten

Course syllabus: Delta Lake and Medallion Architecture for Modern Data Engineering10 Minuten
Understanding the Delta Transaction Log Location in Databricks10 Minuten
Delta Lake Table Properties and Configuration Options10 Minuten

2 AufgabenInsgesamt 21 Minuten

Delta Lake Fundamentals 15 Minuten
Delta Lake Fundamentals6 Minuten

Develop practical PySpark skills to transform, clean, and optimize data for analytics and AI/ML workloads. This module covers essential data transformation techniques, including filtering, aggregations, joins, duplicate handling, null value management, and working with complex data types. Through hands-on exercises, you'll also learn to optimize Delta tables using OPTIMIZE, VACUUM, and Z-Ordering, enabling the creation of efficient, high-quality, and performance-optimized data pipelines.

Das ist alles enthalten

8 Videos1 Lektüre2 Aufgaben

8 VideosInsgesamt 41 Minuten

PySpark Transformations for Data Engineering6 Minuten
Filtering, Selecting, and Renaming Columns4 Minuten
Aggregations and GroupBy Operations4 Minuten
Joining Datasets and Handling Duplicates4 Minuten
Handling Nulls and Applying Data Quality Checks4 Minuten
Transforming Text Data for AI Applications6 Minuten
Delta Table Optimization: OPTIMIZE, VACUUM, and Z-Order6 Minuten
Optimizing Delta Tables for Query Performance5 Minuten

1 LektüreInsgesamt 10 Minuten

Working with Complex Types: Arrays, Structs, and Maps10 Minuten

2 AufgabenInsgesamt 21 Minuten

Data Transformation with Pyspark 15 Minuten
PySpark Data Transformations6 Minuten

Understand how Medallion Architecture structures data into progressive layers for building scalable and reliable analytics and AI/ML pipelines. This module explains the role of Bronze, Silver, and Gold layers, how data is refined at each stage, and how modern workflows are designed for quality and performance. Through hands-on activities, you will ingest raw data, clean and validate it, and prepare business-ready datasets for AI and machine learning use cases.

Das ist alles enthalten

6 Videos3 Lektüren2 Aufgaben

6 VideosInsgesamt 27 Minuten

Medallion Architecture: The Foundation of Modern Data Engineering7 Minuten
The Three Layers of the Medallion Architecture7 Minuten
Setting Up the Medallion Workspace3 Minuten
Ingesting Raw Data into the Bronze Layer3 Minuten
Building the Silver Layer: Cleaning and Validating Data3 Minuten
Building the Gold Layer: Aggregated and Business-Ready Data4 Minuten

3 LektürenInsgesamt 30 Minuten

Designing a Medallion Pipeline for an AI/ML Use Case10 Minuten
Medallion Architecture as a Best Practice for ML Data Pipelines 10 Minuten
Why ML Data Pipelines Extend Naturally to AI Workloads10 Minuten

2 AufgabenInsgesamt 21 Minuten

Medallion Architecture for AI/ML 15 Minuten
Medallion Layers and Data Refinement6 Minuten

Learn the fundamentals of streaming data processing and building reliable data pipelines with Delta Lake. This module introduces Structured Streaming, incremental data processing using Delta Change Data Feed (CDF), and techniques for enforcing data quality with Delta constraints. Through hands-on exercises, you'll build resilient, scalable pipelines capable of processing continuously changing data while maintaining data integrity and reliability.

Das ist alles enthalten

4 Videos2 Lektüren1 Aufgabe

4 VideosInsgesamt 18 Minuten

Introduction to Structured Streaming with Delta Lake7 Minuten
Delta Change Data Feed for Incremental Processing5 Minuten
Enforcing Data Quality with Delta Constraints3 Minuten
Course Summary3 Minuten

2 LektürenInsgesamt 40 Minuten

Streaming Constraints in Databricks Free Edition10 Minuten
Practice project: Building a Reliable Delta Lake Pipeline for MediPlus Clinics30 Minuten

1 AufgabeInsgesamt 30 Minuten

End Course Knowledge Check: Delta Lake & Medallion Architecture30 Minuten

Erwerben Sie ein Karrierezertifikat.

Fügen Sie dieses Zeugnis Ihrem LinkedIn-Profil, Lebenslauf oder CV hinzu. Teilen Sie sie in Social Media und in Ihrer Leistungsbeurteilung.

Dozent

Edureka

234 Kurse215.480 Lernende

von

Edureka

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Häufig gestellte Fragen

You will learn Delta Lake fundamentals, advanced PySpark transformations, Medallion Architecture, data optimization techniques, and streaming concepts using Databricks.

This course is designed for learners who have completed the foundational Data Engineering course or have basic knowledge of Databricks, Spark, and PySpark.

Delta Lake provides ACID transactions, schema enforcement, time travel, and reliable data management, making it the foundation of modern Lakehouse architectures.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.