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Build & Analyze Your Data Lakehouse

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Build & Analyze Your Data Lakehouse

This course is part of multiple programs.

Instructor: Hurix Digital

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

External tables let query engines access distributed files without duplication, reshaping large-scale analytics design.
Choosing Delta, Iceberg, or Hudi requires evaluating schema changes, time travel needs, and performance goals.
Lakehouse architecture merges data lake flexibility with warehouse reliability using metadata and ACID support.
Automated ingestion with staging and transformation layers ensures consistent, high-quality data across analytics systems.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

The modern data landscape demands professionals who can seamlessly bridge the gap between data lakes and data warehouses. This course transforms your ability to architect, implement, and optimize lakehouse platforms that deliver both flexibility and performance.

This Short Course was created to help data engineering professionals accomplish scalable data platform implementation using advanced SQL and lakehouse patterns. By completing this course, you'll be able to register massive file-based datasets as queryable external tables, make informed decisions between Delta Lake, Iceberg, and Hudi formats, and automate robust data ingestion pipelines that keep your warehouse synchronized with your lake. By the end of this course, you will be able to: - Apply configurations to register file-based datasets as external tables - Analyze the technical capabilities of different open-source table formats - Create a data ingestion pipeline within a lakehouse architecture This course is unique because it combines hands-on SQL implementation with strategic architectural decision-making, giving you both the technical skills and analytical framework needed for enterprise-scale data platforms. To be successful in this course, you should have a background in SQL, data warehousing concepts, and distributed systems fundamentals.

Module details

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

What's included

2 videos1 reading2 assignments

2 videosTotal 9 minutes

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

1 readingTotal 10 minutes

External Table Architecture and Implementation Patterns10 minutes

2 assignmentsTotal 21 minutes

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

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

What's included

2 videos1 reading2 assignments

2 videosTotal 13 minutes

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

1 readingTotal 10 minutes

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

2 assignmentsTotal 23 minutes

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

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