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Databricks Certified Data Engineer Professional Training

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Master Databricks with Certified Data Engineer Professional Training by Multisoft Virtual Academy. Learn to build scalable data pipelines, manage transformations, and perform advanced analytics. Benefit from instructor-led sessions, practical labs, and real-world use cases to prepare for certification success and elevate your career in data engineering.

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Instructor-led Training Live Online Classes

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01 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun
02 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun
08 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun
09 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun

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Instructor-led Training Live Online Classes

01 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun
02 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun
08 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun
09 Nov 2025 24 06:00 PM - 09:00 PM Sat, Sun

Course Price At

$ 550

Enroll Now
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Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You’ll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

Course Price At

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Instructor-Led Online Training Parameters

Course Highlights

  • Duration: 24 Hrs.
  • Subject Matter Expert
  • After Training Support
  • Lifetime E-Learning Access
  • Recorded Sessions
  • Free Online Assessments
Databricks Certified Data Engineer Professional Training Online Course Course Syllabus

Curriculum Designed by Experts

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The Databricks Certified Data Engineer Professional Training by Multisoft Virtual Academy is designed to help learners gain the expertise required to design, build, and optimize scalable data pipelines using Databricks. This advanced training program focuses on equipping professionals with the practical knowledge and problem-solving skills needed to handle complex data engineering tasks in real-world environments. Throughout the course, participants will explore core concepts such as data ingestion, transformation, storage optimization, and data quality management. Learners will also gain hands-on experience with Databricks SQL, Delta Lake, Spark, and advanced data modeling techniques. Special emphasis is placed on optimizing performance, ensuring scalability, and implementing robust workflows for modern data-driven enterprises. The program prepares candidates thoroughly for the Databricks Certified Data Engineer Professional exam, with guidance from industry experts and exposure to real-world case studies. By the end of the training, participants will not only be exam-ready but also capable of applying best practices in building efficient data solutions.

Whether you are an aspiring data engineer, a working professional seeking certification, or part of a team modernizing enterprise data platforms, this training offers the right blend of theory, practical labs, and expert mentorship to advance your career in data engineering.

The Databricks Certified Data Engineer Professional Training is an advanced program designed to prepare professionals for building and managing large-scale data solutions on Databricks. It focuses on data pipelines, Delta Lake, Spark, and ML workflows with hands-on projects. The training equips learners with in-demand skills, real-world problem-solving capabilities, and comprehensive exam preparation to successfully achieve the Databricks Certified Data Engineer Professional credential.

  • Explain how Delta Lake uses the transaction log and cloud object storage to guarantee atomicity and durability
  • Describe how Delta Lake’s Optimistic Concurrency Control provides isolation, and which transactions might conflict
  • Describe basic functionality of Delta clone.
  • Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes
  • Implement Delta tables optimized for Databricks SQL service
  • Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)
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  • Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance
  • Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files.
  • Articulate multiple strategies for updating 1+ records in a spark table
  • Implement common design patterns unlocked by Structured Streaming and Delta Lake.
  • Explore and tune state information using stream-static joins and Delta Lake
  • Implement stream-static joins
  • Implement necessary logic for deduplication using Spark Structured Streaming
  • Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read
  • Leverage CDF to easily propagate deletes
  • Demonstrate how proper partitioning of data allows for simple archiving or deletion of data
  • Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries
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  • Describe the objective of data transformations during promotion from bronze to silver
  • Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture
  • Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads.
  • Implement best practices when streaming data from multiplex bronze tables.
  • Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver
  • Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake
  • Implement tables avoiding issues caused by lack of foreign key constraints
  • Add constraints to Delta Lake tables to prevent bad data from being written
  • Implement lookup tables and describe the trade-offs for normalized data models
  • Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads.
  • Implement SCD Type0, 1, and 2 tables
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  • Create Dynamic views to perform data masking
  • Use dynamic views to control access to rows and columns
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  • Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications
  • Inspect event timelines and metrics for stages and jobs performed on a cluster
  • Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications.
  • Design systems that control for cost and latency SLAs for production streaming jobs
  • Deploy and monitor streaming and batch jobs
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  • Adapt a notebook dependency pattern to use Python file dependencies
  • Adapt Python code maintained as Wheels to direct imports using relative paths
  • Repair and rerun failed jobs
  • Create Jobs based on common use cases and patterns
  • Create a multi-task job with multiple dependencies
  • Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters
  • Execute commands from the CLI to deploy and monitor Databricks jobs
  • Use REST API to clone a job, trigger a run, and export the run output
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Databricks Certified Data Engineer Professional Training Online Course Description

  • Understand the Databricks architecture, ecosystem, and its role in modern data engineering.
  • Design, build, and manage scalable and reliable data pipelines.
  • Apply Apache Spark for high-performance distributed data processing.
  • Implement and optimize Delta Lake for data reliability, consistency, and ACID transactions.
  • Integrate machine learning workflows within data engineering pipelines.
  • Perform advanced data ingestion, transformation, and orchestration techniques.
  • Optimize performance and troubleshoot complex data processing workflows.
  • Implement governance, security, and monitoring for enterprise-scale data platforms.

  • Data Engineers
  • Data Analysts
  • Data Architects
  • Big Data Professionals
  • Machine Learning Engineers
  • ETL Developers
  • Business Intelligence (BI) Professionals
  • Cloud Engineers working on Azure, AWS, or GCP with Databricks
  • IT Professionals transitioning into data engineering

  • Basic understanding of SQL and relational databases
  • Familiarity with Python or Scala programming languages
  • Knowledge of data engineering concepts (ETL, data pipelines, data warehousing)

Databricks Certified Data Engineer Professional Training Online Course Certification

Multisoft Virtual Academy provides a globally recognized training certificate to the participants, after successful completion of a training program. The training certificates are recognized and accepted across the world.

Multisoft Virtual Academy's training certificate comes with lifetime validity.

Aspirants can directly enroll for the desired course from the Book Now Button in the course page. You can also connect on Whatsapp at +91 8130666206 to talk with a training advisor. Multisoft Virtual Academy also offers customized training programs on a wide range of domains and skills.

All training programs offered by Multisoft Virtual Academy are delivered by certified industry experts, who have years of experience in the relevant domains. Multisoft Global Subject Matter Experts impart knowledge on a wide variety of training courses through one –on-one and corporate training sessions.

Multisoft Virtual Academy training certification can help participants stand out in the competitive job market. Since the training certificates are internationally accepted, participants can showcase their skills and knowledge to employers across the world.

Databricks Certified Data Engineer Professional Online Course Corporate Training Certification

Interactive Virtual Training

Interactive Virtual Training

  • Global Subject Matter Experts
  • Step-by –Step Learning Approach
  • Instant Doubt Clearing
Lifetime Access

Lifetime Access

  • Lifetime E-learning Access
  • Recorded Training Session Videos
  • Free Access to Practice Tests
24x7 Assistance

24x7 Assistance

  • Help Desk Support
  • Doubt Resolution in Real-time
  • After Training Support
Hands on Experience

Hands on Experience

  • Project Based Learning
  • Learning based on real-life examples
  • Assignments and Practice Tests
Globally Recognized Training Certificate

Globally Recognized Certificate

  • Multisoft Training Certificate
  • Globally Recognized and Accepted
  • Lifetime Validity

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Databricks Certified Data Engineer Professional Training Online Course FAQ's

Yes, participants receive a training certificate from Multisoft Virtual Academy, validating their skills and successful course completion. 

The training is hands-on with real-world projects and case studies to ensure practical, job-ready skills.

You will learn to design and optimize scalable data pipelines, manage transformations with Spark and Delta Lake, ensure data quality, implement workflow orchestration, and apply best practices for real-world data engineering projects.

The training is conducted online with live, instructor-led sessions, hands-on labs, assignments, and real-world case studies to ensure practical learning.

To contact Multisoft Virtual Academy you can mail us on enquiry@multisoftvirtualacademy.com or can call for course enquiry on this number  +91 8130666206

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