Databricks Certified Data Engineer Professional Training

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  • Instructor-led Online Training
  • Project Based Learning
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Databricks Certified Data Engineer Professional Training Course Overview

Master Databricks Certified Data Engineer Professional Training with Multisoft Systems and build job-ready expertise in advanced data engineering. Learn to design scalable pipelines, optimize data processing with Delta Lake and Spark, and manage ML workflows. Our expert-led training ensures hands-on practice and certification success for career growth in data engineering.

The Databricks Certified Data Engineer Professional Training by Multisoft Systems is designed to equip professionals with the advanced knowledge and hands-on expertise required to excel in data engineering. This training focuses on building, optimizing, and managing large-scale data pipelines using Databricks and its integrated ecosystem. Participants will learn to design scalable and secure data workflows, leverage Apache Spark for high-performance data processing, and implement Delta Lake to ensure reliability and consistency across data operations. The program also emphasizes modern data engineering practices such as data ingestion, transformation, orchestration, and machine learning pipeline integration. With real-world case studies, practical assignments, and expert-led sessions, learners will gain the confidence to tackle complex data challenges in enterprise environments. By covering performance optimization, monitoring, governance, and advanced automation, the course ensures participants develop both technical depth and problem-solving skills.

This training is ideal for data engineers, analysts, architects, and IT professionals aiming to advance their careers in big data and analytics. By the end of the program, participants will be fully prepared for the Databricks Certified Data Engineer Professional exam and ready to contribute effectively to data-driven projects and innovation.

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Databricks Certified Data Engineer Professional Training Course curriculum

Curriculum Designed by Experts

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Master Databricks Certified Data Engineer Professional Training with Multisoft Systems and build job-ready expertise in advanced data engineering. Learn to design scalable pipelines, optimize data processing with Delta Lake and Spark, and manage ML workflows. Our expert-led training ensures hands-on practice and certification success for career growth in data engineering.

The Databricks Certified Data Engineer Professional Training by Multisoft Systems is designed to equip professionals with the advanced knowledge and hands-on expertise required to excel in data engineering. This training focuses on building, optimizing, and managing large-scale data pipelines using Databricks and its integrated ecosystem. Participants will learn to design scalable and secure data workflows, leverage Apache Spark for high-performance data processing, and implement Delta Lake to ensure reliability and consistency across data operations. The program also emphasizes modern data engineering practices such as data ingestion, transformation, orchestration, and machine learning pipeline integration. With real-world case studies, practical assignments, and expert-led sessions, learners will gain the confidence to tackle complex data challenges in enterprise environments. By covering performance optimization, monitoring, governance, and advanced automation, the course ensures participants develop both technical depth and problem-solving skills.

This training is ideal for data engineers, analysts, architects, and IT professionals aiming to advance their careers in big data and analytics. By the end of the program, participants will be fully prepared for the Databricks Certified Data Engineer Professional exam and ready to contribute effectively to data-driven projects and innovation.

  • 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.

Course Prerequisite

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

Course Target Audience

  • 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

Course Content

  • 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 (MCQ) Assessment

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Databricks Certified Data Engineer Professional Training FAQ's

This training equips professionals with advanced skills to design, build, and manage scalable data pipelines using Databricks, Spark, and Delta Lake, preparing them for the official certification exam.

Data engineers, analysts, architects, ETL developers, BI professionals, cloud engineers, and anyone preparing for the Databricks Certified Data Engineer Professional exam.

Participants should have basic SQL knowledge, familiarity with Python/Scala, an understanding of ETL processes, and hands-on experience with Spark. Prior Databricks exposure is recommended.

Certified professionals can pursue roles such as Data Engineer, Big Data Engineer, ETL Developer, Cloud Data Engineer, and other advanced data engineering positions across industries.

To contact Multisoft Systems you can mail us on info@multisoftsystems.com or can call for course enquiry on this number +91 9810306956

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