Databricks Certified Data Engineer Professional
The Databricks Certified Data Engineer Professional training enables data professionals to deepen their expertise in advanced data engineering using Databricks, Apache Spark, and the Lakehouse architecture. Participants will learn how to design, optimize, and orchestrate complex data pipelines, manage large-scale batch and streaming workloads, and fully leverage advanced Delta Lake capabilities within modern Big Data environments.
- Reference : 1528
- Duration : 3 Days
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What you will learn
- Master the syntax, data structures and paradigms of the studied language
- Design and develop functional end-to-end applications
- Apply coding best practices and SOLID or equivalent principles
- Use versioning, debugging and testing tools in your projects
- Integrate APIs, databases and external services into your developments
- Complete a full project demonstrating the skills acquired during training
About this course
Databricks Certified Data Engineer Professional — What you need to know
Total duration
Databricks Certified Data Engineer Professional
Modern organizations generate and process massive volumes of data from multiple sources, requiring scalable platforms capable of supporting high-performance data processing and analytics.
The Databricks platform, built on Apache Spark and the Lakehouse architecture, enables organizations to design powerful data pipelines capable of processing and transforming large datasets efficiently in cloud and Big Data environments.
The Databricks Certified Data Engineer Professional training equips participants with advanced skills to design, optimize, and manage complex data pipelines, ensuring data reliability, performance, and governance.
During this training, participants will learn to:
• Design advanced data engineering architectures using Databricks
• Develop and orchestrate complex automated data pipelines
• Optimize distributed data processing using Apache Spark
• Leverage advanced features of Delta Lake
• Implement data ingestion, transformation, and streaming solutions
• Apply data governance and security strategies
• Monitor, troubleshoot, and optimize data pipeline performance
Who is this course for?
Target profiles and expected levels
Developers looking to deepen their technical skills in a language or framework
Computer science students looking to complement their academic training with practice
Professionals transitioning to software development roles
Data scientists and analysts looking to automate their data processing
IT engineers looking to modernise their skills with the latest technologies
Anyone looking to develop a concrete application or IT project
Course Program
Lakehouse Architecture and Advanced Data Engineering
1 modules- 01Lakehouse Architecture and Advanced Data EngineeringOverview of Databricks Lakehouse architecture and advanced data engineering concepts Managing Databricks environments and cluster configuration Optimizing distributed processing with Apache Spark Advanced management of DataFrames and Spark SQL Data ingestion techniques from multiple sources Designing scalable and resilient data pipeline architectures
Why Choose Our Course?
What sets us apart from other training centers
Project-oriented training with concrete exercises at each stage of learning
Expert trainers actively practising in the software development industry
Preconfigured development environment provided to get started immediately
Small groups enabling personalised follow-up and quality exchanges
Access to additional resources and an active community after training
Programme constantly updated to reflect current industry standards
Frequently Asked Questions (FAQ)
Everything you need to know before enrolling
Experience with Databricks or Apache Spark and solid knowledge of SQL or Python is recommended.
The program is delivered by Databricks-certified instructors and experienced data engineering specialists.
You will develop advanced skills in Big Data pipeline design, Spark optimization, and modern analytics platform management, which are highly valued in data engineering roles.
