Databricks Certified Data Engineer Professional -Preparation — 93% OFF Discount Coupon

UPDATED: 2026 | Preparation course for Databricks Data Engineer Professional certification exam with hands-on training

★4.6 out of 5
32,939 students
Created by Derar Alhussein | 8x Databricks Certified
English
Updated April 2026

Quick Facts — Course Summary

Here's a quick overview of everything you need to know about Databricks Certified Data Engineer Professional -Preparation before you enroll:

•Course Name: Databricks Certified Data Engineer Professional -Preparation
•Platform: Udemy
•Instructor: Derar Alhussein | 8x Databricks Certified
•Coupon Last Verified: April 24, 2026
•Level: Advanced
•Topic: IT & Software
•Subtopic: IT Certifications
•Total Time: 3h of video content
•Language: English
•Access Type: Unlimited lifetime access + updates
•Certificate: Included upon completion from Udemy
•Main Skills: Learn how to model data management solutions on Databricks Lakehouse · Build data processing pipelines using the Spark and Delta Lake APIs · Understand how to use and the benefits of using the Databricks platform and its tools
•Requirements: MUST HAVE - All the skills of an "Associate" Data Engineer on Databricks platform · If you feel you lack these skills, you should first study my preparation course on Udemy for the Associate-level certification. I covered there all the fundamental concepts of Databricks Lakehouse with hands-on training.
•Current Price: $11.99 (was $159.99). You save $148.00 with 93% discount.
•How to Apply: Click the coupon button to activate your discount automatically
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Skills You'll Master

By the end of Databricks Certified Data Engineer Professional -Preparation, you'll have these practical skills:

✓Learn how to model data management solutions on Databricks Lakehouse .
✓Build data processing pipelines using the Spark and Delta Lake APIs .
✓Understand how to use and the benefits of using the Databricks platform and its tools .
✓Build production pipelines using best practices around security and governance .
✓Learn how to monitor and log production jobs .
✓Follow best practices for deploying code on Databricks.

What You Need Before Starting

Before enrolling in Databricks Certified Data Engineer Professional -Preparation, make sure you have:

•MUST HAVE - All the skills of an "Associate" Data Engineer on Databricks platform
•If you feel you lack these skills, you should first study my preparation course on Udemy for the Associate-level certification. I covered there all the fundamental concepts of Databricks Lakehouse with hands-on training.

About This Udemy Course

The following is the full official course description for Databricks Certified Data Engineer Professional -Preparation as published on Udemy by instructor Derar Alhussein | 8x Databricks Certified:

If you are interested in becoming a Certified Data Engineer Professional from Databricks, you have come to the right place! This study guide will help you with preparing for this certification exam.

By the end of this course, you should be able to:

1- Develop Code for Data Processing using Python and SQL

Using Python and Tools for development
  • Design and implement a scalable Python project structure optimized for Databricks Asset Bundles (DABs), enabling modular development, deployment automation, and CI/CD integration.
  • Manage and troubleshoot external third-party library installations and dependencies in Databricks, including PyPI packages, local wheels, and source archives.
  • Develop User-Defined Functions (UDFs) using Pandas/Python UDF

Building and Testing an ETL pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark on the Databricks platform
  • Build and manage reliable, production-ready data pipelines, for batch and streaming data using Lakeflow Declarative Pipelines and Autoloader.
  • Create and Automate ETL workloads using Jobs via UI/APIs/CLI.
  • Explain the advantages and disadvantages of streaming tables compared to materialized views.
  • Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines.
  • Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for building scalable ETL pipelines. ● Create a pipeline component that uses control flow operators (e.g. if/else, foreach, etc.)
  • Choose the appropriate configs for environments and dependencies, high memory for notebook tasks, and auto-optimization to disallow retries.
  • Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, and testing frameworks, to ensure code correctness, including a built-in debugger.

2- Data Ingestion & Acquisition:
  • Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.
  • Create an append-only data pipeline capable of handling both batch and streaming data using Delta.

3- Data Transformation, Cleansing, and Quality
  • Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.
  • Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or autoloader in classic jobs.

4- Data Sharing and Federation
  • Demonstrate delta sharing securely between Databricks deployments using Databricks to Databricks Sharing(D2D) or to external platforms using open sharing protocol(D2O).
  • Configure Lakehouse Federation with proper governance across supported source Systems.
  • Use Delta Share to share live data from Lakehouse to any computing platform.

5- Monitoring and Alerting
  • Monitoring
  • Use system tables for observability over resource utilization, cost, auditing and workload monitoring.
  • Use Query Profiler UI and Spark UI to monitor workloads. ● Use the Databricks REST APIs/Databricks CLI for monitoring jobs and pipelines.
  • Use Lakeflow Declarative Pipelines Event Logs to monitor pipelines.
  • Alerting
  • Use SQL Alerts to monitor data quality.
  • Use the Workflows UI and Jobs API to set up job status and performance issue notifications.

6- Cost & Performance Optimisation
  • Understand how / why using Unity Catalog managed tables reduces operation Overhead and maintenance burden.
  • Understand delta optimization techniques, such as deletion vectors and liquid clustering.
  • Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc).
  • Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.
  • Use query profile to analyze the query and identify bottlenecks, such as bad data kipping, inefficient types of joins, data shuffling.

7- Ensuring Data Security and Compliance
  • Applying Data Security mechanisms.
  • Use ACLs to secure Workspace Objects, enforcing the principle of least privilege, including enforcing principles like least privilege, policy enforcement.
  • Use row filters and column masks to filter and mask sensitive table data.
  • Apply anonymization and pseudonymization methods such as Hashing, Tokenization, Suppression, and Generalization to confidential data.
  • Ensuring Compliance
  • Implement a compliant batch & streaming pipeline that detects and applies masking of PII to ensure data privacy.
  • Develop a data purging solution ensuring compliance with data retention policies.

8- Data Governance
  • Create and add descriptions/metadata about enterprise data to make it more discoverable.
  • Demonstrate understanding of Unity Catalog permission inheritance model.

9- Debugging and Deploying
  • Debugging and Troubleshooting
  • Identify pertinent diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors.
  • Analyze the errors and remediate the failed job runs with job repairs and parameter overrides.
  • Use Lakeflow Declarative Pipelines event logs & the Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines.
  • Deploying CI/CD
  • Build and Deploy Databricks resources using Databricks Asset Bundles.
  • Configure and integrate with Git-based CI/CD workflows using Databricks Git Folders for notebook and code deployment.

10- Data Modelling
  • Design and implement scalable data models using Delta Lake to manage large datasets.
  • Simplify data layout decisions and optimize query performance using Liquid Clustering.
  • Identify the benefits of using liquid Clustering over Partitioning and Z-Order.
  • Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.
With the knowledge you gain during this course, you will be ready to take the certification exam.

I am looking forward to meeting you!

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Is the Databricks Certified Data Engineer Professional -Preparation Coupon Worth It?

Expert review by Andrew Derek, Lead Course Analyst at CoursesWyn.•Last updated: April 24, 2026.

Based on analysis of the curriculum structure, student engagement metrics, and verified rating data, Databricks Certified Data Engineer Professional -Preparation is a high-value resource for learners seeking to build skills inIT & Software. Taught by Derar Alhussein | 8x Databricks Certified on Udemy, the 3h course provides a structured progression from foundational concepts to advanced techniques— making it suitable for learners at all levels. The current coupon reduces the price by 93%, from $159.99 to $11.99, removing the primary financial barrier to enrollment.

✓What We Like (Pros)

  • Verified 93% price reduction makes this course accessible to learners on any budget.
  • Aggregate student rating of 4.6 out of 5 indicates high learner satisfaction.
  • Strong enrollment base with over 32,939 students demonstrates course popularity and trust.
  • Includes an official Udemy completion certificate and lifetime access to all future content updates.

!Keep in Mind (Cons)

The following limitations should be considered before enrolling in Databricks Certified Data Engineer Professional -Preparation:

  • The depth of IT & Software coverage may be challenging for absolute beginners without the listed prerequisites.
  • Lifetime access is contingent on the continued operation of the Udemy platform.
  • Hands-on projects and quizzes require additional time investment beyond video watch time.
Final Verdict: Worth It
This course offers exceptional value with current pricing
✓

Course Rating Summary

Databricks Certified Data Engineer Professional -Preparation Course holds an aggregate rating of 4.6 out of 5 based on 32,939 student reviews on Udemy.

4.6
★★★★★
32,939 Verified Ratings
5 stars
75%
4 stars
15%
3 stars
6%
2 stars
2%
1 star
2%

* Rating distribution is approximated from the aggregate score. Sourced from Udemy.

Instructor Profile

The following section provides background information on Derar Alhussein | 8x Databricks Certified, the instructor responsible for creating and maintaining Databricks Certified Data Engineer Professional -Preparation on Udemy.

Databricks Certified Data Engineer Professional -Preparation is taught by Derar Alhussein | 8x Databricks Certified, a Udemy instructor specializing in IT & Software. For the full instructor biography, professional credentials, and a complete list of their courses, visit the official instructor profile on Udemy.

•Instructor Name: Derar Alhussein | 8x Databricks Certified
•Subject Area: IT & Software
•Teaching Approach: Practical, project-based instruction focused on real-world application of IT & Software skills.

Frequently Asked Questions

The following questions and answers cover the most common queries about Databricks Certified Data Engineer Professional -Preparation, its coupon code, pricing, and enrollment process.

About the Author

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Andrew Derek

Lead Course Analyst at CoursesWyn with 8+ years of experience evaluating online learning platforms. I've analyzed 500+ Udemy courses and helped thousands of learners choose the right courses for their career goals.

⭐4.8/5 Rating
✓Trusted by 10K+ Students

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