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LakeFS

Master LakeFS to build reliable, reproducible, and scalable data lakes with Git-like versioning, branching, and governance for modern data platforms.
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Save 59% Offer ends on 31-Dec-2025
Course Duration: 10 Hours
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As organizations increasingly rely on data lakes to power analytics, machine learning, and AI-driven decision-making, ensuring data reliability, reproducibility, and governance has become a critical challenge. Traditional data lakes built on object storage systems such as Amazon S3, Azure Blob Storage, or Google Cloud Storage lack native version control mechanisms. This makes it difficult to track changes, roll back errors, manage concurrent workflows, or guarantee reproducibility across data pipelines.
 
LakeFS addresses this challenge by introducing Git-like version control for data lakes. It allows teams to apply proven software engineering practices—such as branching, commits, merges, and rollbacks—to large-scale data stored in object storage. With LakeFS, data engineers, analytics teams, and ML practitioners can collaborate safely, experiment freely, and deploy data changes with confidence.
 
LakeFS acts as a control plane on top of your existing data lake, enabling transactional consistency and data lineage without copying or duplicating data. It integrates seamlessly with popular data tools and engines such as Apache Spark, Trino, Presto, Hive, Airflow, dbt, Flink, and machine learning frameworks, making it a foundational component of modern data platforms.
 
The LakeFS course by Uplatz provides a comprehensive, end-to-end learning experience designed for data engineers, platform engineers, and analytics professionals. You will learn not only how LakeFS works internally, but also how to deploy, configure, integrate, and operate LakeFS in real-world production environments. The course emphasizes hands-on implementation, enterprise use cases, and best practices for building robust data lake architectures.
 
By the end of this course, learners will be able to design data pipelines that are safe, auditable, reproducible, and production-ready, even in complex, multi-team environments.
🔍 What Is LakeFS?
 
LakeFS is an open-source data lake version control system that brings Git-like operations to object storage–based data lakes. Instead of versioning files manually or duplicating datasets for testing, LakeFS allows you to:

  • Create branches for experimentation

  • Commit data changes atomically

  • Merge validated data into production

  • Roll back faulty data updates instantly

  • Track data lineage and history

LakeFS works with existing object storage systems including:

  • Amazon S3

  • Azure Data Lake Storage (ADLS)

  • Google Cloud Storage (GCS)

  • MinIO and other S3-compatible storage

Because LakeFS is storage-agnostic and tool-agnostic, it fits naturally into modern cloud, hybrid, and on-prem data architectures.
⚙️ How LakeFS Works
 
LakeFS introduces a metadata layer on top of object storage, enabling versioned views of data without duplicating physical files.
 
1. Git-Like Branching Model
 
LakeFS allows teams to create isolated branches of a data lake:

  • main or production branch for trusted data

  • Feature branches for experiments

  • Staging branches for validation

Each branch represents a consistent snapshot of the data lake.
2. Atomic Commits
 
All changes to data are committed atomically. This ensures:

  • Partial writes are never exposed

  • Downstream jobs always read consistent data

  • Failed pipelines do not corrupt production datasets

3. Zero-Copy Architecture
 
LakeFS does not duplicate data. Instead, it uses metadata pointers to reference objects efficiently. This enables:

  • Fast branching

  • Minimal storage overhead

  • Cost-efficient experimentation

4. Safe Merges and Rollbacks
 
Once data is validated, it can be merged safely into production. If something goes wrong, LakeFS allows instant rollback to a previous state—just like Git.
5. Governance and Auditing
 
LakeFS tracks:

  • Who changed what data

  • When changes occurred

  • Which pipeline produced which dataset

This is essential for compliance, auditing, and enterprise governance.
🏭 Where LakeFS Is Used in Industry
 
LakeFS is widely adopted across industries where data reliability and scale matter.
 
1. Data Engineering Pipelines
 
Ensure safe ETL/ELT workflows with rollback support for failed jobs.
 
2. Machine Learning & MLOps
 
Enable reproducible training datasets and controlled experimentation.
 
3. Analytics & BI
 
Prevent broken dashboards caused by partial or corrupted data updates.
 
4. Finance & Banking
 
Maintain strong data governance, lineage, and auditability.
 
5. Healthcare & Life Sciences
 
Ensure regulatory compliance and reproducible analytics.
 
6. Data Science Collaboration
 
Allow multiple teams to work on shared datasets without conflicts.
 
LakeFS is especially valuable in multi-team, multi-pipeline environments where data correctness is critical.
🌟 Benefits of Learning LakeFS
 
By mastering LakeFS, learners gain:

  • Strong data engineering best practices

  • Git-style workflows for data lakes

  • Ability to build reliable and reproducible pipelines

  • Expertise in modern data platform architecture

  • Experience with enterprise-grade governance tools

  • Skills highly valued in data engineering and MLOps roles

LakeFS knowledge is becoming a must-have skill for advanced data teams.
📘 What You’ll Learn in This Course
 
You will learn how to:

  • Understand LakeFS architecture and design

  • Deploy LakeFS on cloud and on-prem environments

  • Use branches, commits, merges, and rollbacks

  • Integrate LakeFS with Spark, Airflow, and dbt

  • Build safe ETL pipelines with atomic writes

  • Manage data experimentation workflows

  • Implement data governance and auditing

  • Support ML and analytics use cases

  • Operate LakeFS in production environments

  • Design enterprise-grade data lake platforms

🧠 How to Use This Course Effectively

  • Start with basic LakeFS concepts and CLI usage

  • Practice branching and committing data changes

  • Integrate LakeFS into ETL pipelines

  • Experiment with failure recovery and rollbacks

  • Apply governance and auditing features

  • Complete the capstone project with a real-world scenario

👩‍💻 Who Should Take This Course
 
This course is ideal for:

  • Data Engineers

  • Analytics Engineers

  • Platform Engineers

  • Machine Learning Engineers

  • Data Architects

  • Cloud Engineers

  • DevOps and MLOps professionals

  • Students entering data engineering roles

🚀 Final Takeaway
 
LakeFS transforms data lakes from fragile storage systems into reliable, version-controlled data platforms. By bringing software engineering discipline to data workflows, LakeFS enables teams to move faster without sacrificing safety, governance, or reproducibility.
 
This course equips learners with the skills needed to design, operate, and scale modern data lakes that support analytics, AI, and enterprise workloads with confidence.

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand LakeFS architecture and internals

  • Apply Git-like versioning to data lakes

  • Build safe and reproducible data pipelines

  • Integrate LakeFS with modern data tools

  • Implement governance and rollback strategies

  • Operate LakeFS in enterprise environments

    Course Syllabus

    Module 1: Introduction to LakeFS

    • Data lake challenges

    • Why version control for data

    Module 2: LakeFS Architecture

    • Metadata layer

    • Storage integration

    Module 3: Core Concepts

    • Branches, commits, merges

    • Rollbacks and snapshots

    Module 4: Installing & Running LakeFS

    • Local setup

    • Cloud deployment

    Module 5: Integrating with Data Tools

    • Spark

    • Airflow

    • dbt

    Module 6: Safe ETL Pipelines

    • Atomic writes

    • Failure recovery

    Module 7: Governance & Auditing

    • Data lineage

    • Compliance workflows

    Module 8: LakeFS for ML & Analytics

    • Reproducible datasets

    • Experiment tracking

    Module 9: Production Best Practices

    • Scaling

    • Security and access control

    Module 10: Capstone Project

    • Build a version-controlled enterprise data lake

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to LakeFS

  • Data lake challenges

  • Why version control for data

Module 2: LakeFS Architecture

  • Metadata layer

  • Storage integration

Module 3: Core Concepts

  • Branches, commits, merges

  • Rollbacks and snapshots

Module 4: Installing & Running LakeFS

  • Local setup

  • Cloud deployment

Module 5: Integrating with Data Tools

  • Spark

  • Airflow

  • dbt

Module 6: Safe ETL Pipelines

  • Atomic writes

  • Failure recovery

Module 7: Governance & Auditing

  • Data lineage

  • Compliance workflows

Module 8: LakeFS for ML & Analytics

  • Reproducible datasets

  • Experiment tracking

Module 9: Production Best Practices

  • Scaling

  • Security and access control

Module 10: Capstone Project

  • Build a version-controlled enterprise data lake

Certification Back to Top

Upon completion, learners receive a Uplatz Certificate in LakeFS & Data Lake Version Control, validating expertise in modern data engineering and governance.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • Data Engineer

  • Analytics Engineer

  • Platform Engineer

  • MLOps Engineer

  • Data Architect

  • Cloud Data Engineer

Interview Questions Back to Top

  1. What is LakeFS?
    A Git-like version control system for data lakes.

  2. Which storage systems does LakeFS support?
    S3, ADLS, GCS, and S3-compatible storage.

  3. Does LakeFS duplicate data?
    No, it uses a zero-copy metadata approach.

  4. Why is branching useful in data lakes?
    It enables safe experimentation and isolation.

  5. Can LakeFS roll back failed pipelines?
    Yes, instantly.

  6. Which tools integrate with LakeFS?
    Spark, Airflow, dbt, Trino, and more.

  7. Is LakeFS open source?
    Yes.

  8. What problem does LakeFS solve?
    Data corruption, lack of reproducibility, and unsafe pipelines.

  9. Is LakeFS suitable for ML workflows?
    Yes, especially for reproducible training data.

  10. Why is LakeFS enterprise-ready?
    Governance, auditing, and scalability.

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