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

Ce cours fait partie de Spécialisation "Data Engineering for AI and ML Pipelines"

Instructeur : Edureka

Inclus avec En savoir plus

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

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

Outils que vous découvrirez

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

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

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juillet 2026

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Ce cours fait partie de la Spécialisation "Data Engineering for AI and ML Pipelines"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
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Il y a 4 modules dans ce cours

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.

Inclus

11 vidéos3 lectures2 devoirs

11 vidéosTotal 63 minutes

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

3 lecturesTotal 30 minutes

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

2 devoirsTotal 21 minutes

Delta Lake Fundamentals 15 minutes
Delta Lake Fundamentals6 minutes

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.

Inclus

8 vidéos1 lecture2 devoirs

8 vidéosTotal 41 minutes

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

1 lectureTotal 10 minutes

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

2 devoirsTotal 21 minutes

Data Transformation with Pyspark 15 minutes
PySpark Data Transformations6 minutes

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.

Inclus

6 vidéos3 lectures2 devoirs

6 vidéosTotal 27 minutes

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

3 lecturesTotal 30 minutes

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

2 devoirsTotal 21 minutes

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

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.

Inclus

4 vidéos2 lectures1 devoir

4 vidéosTotal 18 minutes

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

2 lecturesTotal 40 minutes

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

1 devoirTotal 30 minutes

End Course Knowledge Check: Delta Lake & Medallion Architecture30 minutes

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Edureka

234 Cours215 948 apprenants

Offert par

Edureka

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Foire Aux Questions

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.