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Azure Databricks Data Engineer DP-750 Training in Canada Course Syllabus

Curriculum Designed by Experts

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The Microsoft Certified: Azure Databricks Data Engineer Associate Certification (Exam DP-750) Training program provides comprehensive knowledge of designing, implementing, and managing modern data engineering solutions using Azure Databricks and Apache Spark. The course covers Azure Databricks workspaces, Apache Spark programming, Delta Lake, Spark SQL, data ingestion, ETL and ELT pipeline development, Structured Streaming, lakehouse architecture, workflow orchestration, Unity Catalog, cluster administration, performance tuning, security, governance, and integration with Azure data services. Participants will learn how to build scalable batch and real-time data processing pipelines, optimize distributed computing workloads, implement enterprise data governance, and deliver high-performance analytics solutions. Through hands-on labs, real-world implementation scenarios, and certification-focused projects, learners will gain the practical expertise required to develop reliable, secure, and enterprise-grade data engineering platforms while preparing for the Microsoft Certified: Azure Databricks Data Engineer Associate Certification (Exam DP-750).

  • DP-750 Certification Overview
  • Azure Databricks Architecture
  • Lakehouse Fundamentals
  • Workspace Configuration
  • Databricks User Interface
  • Workspace Administration
  • Databricks Runtime
  • Enterprise Use Cases
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  • Apache Spark Architecture
  • Spark Clusters
  • Spark DataFrames
  • Spark SQL
  • Spark Transformations
  • Spark Actions
  • Partitioning
  • Performance Basics
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  • Databricks Notebooks
  • Python for Databricks
  • SQL Development
  • Notebook Collaboration
  • Widgets
  • Magic Commands
  • Workspace Assets
  • Source Control Integration
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  • Batch Data Ingestion
  • Incremental Loading
  • Auto Loader
  • Data Validation
  • Data Cleansing
  • ETL Pipelines
  • ELT Workflows
  • Data Transformation
  • Error Handling
  • Data Quality Checks
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  • Delta Lake Architecture
  • ACID Transactions
  • Schema Enforcement
  • Schema Evolution
  • Time Travel
  • Delta Tables
  • Delta Optimization
  • Vacuum Operations
  • Data Versioning
  • Best Practices
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  • Complex Transformations
  • Joins and Aggregations
  • Window Functions
  • User Defined Functions (UDFs)
  • Data Serialization
  • Nested Data Processing
  • Semi-Structured Data
  • Performance Optimization
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  • Streaming Architecture
  • Structured Streaming
  • Streaming Sources
  • Streaming Sinks
  • Checkpointing
  • Watermarking
  • Event Processing
  • Fault Tolerance
  • Streaming Optimization
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  • Databricks Workflows
  • Job Scheduling
  • Pipeline Automation
  • Task Dependencies
  • Parameter Management
  • Notifications
  • Retry Policies
  • Workflow Monitoring
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  • Unity Catalog
  • Data Access Control
  • Identity Management
  • Credential Management
  • Data Lineage
  • Audit Logging
  • Encryption
  • Governance Best Practices
  • Regulatory Compliance
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  • Cluster Configuration
  • Cluster Policies
  • Spark Optimization
  • Query Optimization
  • Caching
  • Partition Strategies
  • Resource Scaling
  • Cost Optimization
  • Performance Monitoring
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  • Azure Data Lake Storage
  • Azure Data Factory
  • Azure Synapse Analytics
  • Azure Event Hubs
  • Azure Key Vault
  • Power BI Integration
  • Azure Monitor
  • Microsoft Fabric Connectivity
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  • Deployment Strategies
  • Monitoring
  • Logging
  • Troubleshooting
  • Backup Strategies
  • Disaster Recovery
  • Operational Best Practices
  • Production Readiness
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  • End-to-End Data Pipeline
  • Lakehouse Implementation
  • Streaming Analytics Project
  • Enterprise Data Governance
  • Performance Tuning
  • Security Implementation
  • DP-750 Certification Practice Labs
  • Capstone Data Engineering Project
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Azure Databricks Data Engineer DP-750 Training in Canada Description

  • Develop expertise in designing and implementing scalable data engineering solutions using Azure Databricks and Apache Spark for enterprise analytics.
  • Learn how to build efficient batch and real-time data pipelines that ingest, process, transform, and deliver high-quality data across modern cloud platforms.
  • Gain practical knowledge of Apache Spark components, including DataFrames, Spark SQL, Structured Streaming, and distributed data processing techniques.
  • Master Delta Lake capabilities such as ACID transactions, schema evolution, time travel, data versioning, and optimized lakehouse architecture.
  • Learn to create robust ETL and ELT workflows that integrate data from multiple cloud, on-premises, and streaming data sources.
  • Build proficiency in implementing workflow orchestration, job scheduling, and automated pipeline execution using Azure Databricks Workflows.
  • Configure Unity Catalog to manage enterprise data governance, metadata, access control, auditing, and secure data sharing across analytics environments.
  • Optimize Spark clusters, storage formats, partitioning strategies, query execution, and resource utilization to maximize performance and reduce operational costs.
  • Integrate Azure Databricks with Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Azure Event Hubs, Power BI, and other Azure services to support end-to-end analytics solutions.
  • Learn to monitor, troubleshoot, secure, and maintain enterprise data engineering workloads while implementing best practices for reliability, scalability, and operational excellence.
  • Prepare for the Microsoft Certified: Azure Databricks Data Engineer Associate Certification (Exam

DP-750) through practical implementation aligned with the latest certification objectives and real-world enterprise scenarios.

  • Apply industry best practices to design modern lakehouse architectures, develop production-read

  • Azure Databricks Data Engineers responsible for designing and managing cloud-based data platforms 
  • Apache Spark Developers building distributed data processing and analytics solutions 
  • Azure Data Engineers implementing scalable ETL, ELT, and lakehouse architectures on Microsoft Azure 
  • Big Data Engineers working with large-scale batch processing, streaming data, and cloud-native analytics platforms 
  • Data Integration Specialists developing automated data pipelines and enterprise data workflows 
  • Analytics Engineers preparing trusted, high-quality datasets for business intelligence, reporting, and advanced analytics initiatives 
  • Cloud Data Platform Engineers responsible for optimizing Azure Databricks environments, clusters, and data processing workloads 
  • Data Warehouse Professionals transitioning to modern lakehouse architecture and cloud-based data engineering solutions 
  • Solution Architects designing secure, scalable, and high-performance enterprise data platforms using Azure Databricks and Apache Spark 
  • Professionals preparing for the Microsoft Certified: Azure Databricks Data Engineer Associate Certification (Exam DP-750) to validate their expertise in cloud data engineering and Azure Databricks development 
  • Organizations implementing modern data platforms that require skilled professionals to build, optimize, govern, and maintain enterprise data pipelines on Azure 
  • IT professionals seeking hands-on expertise in Azure Databricks, Delta Lake, Spark SQL, Structured Streaming, data governance, and enterprise-scale cloud data engineering. 

  • A basic understanding of database concepts, SQL queries, and relational database management systems is recommended.
  • Familiarity with Microsoft Azure fundamentals, cloud computing concepts, and Azure storage services will help participants understand the Azure Databricks ecosystem more effectively.
  • Knowledge of programming languages such as Python, SQL, or Scala is beneficial for developing Databricks notebooks and Apache Spark applications.
  • Basic understanding of data engineering concepts, including ETL/ELT processes, data transformation, and data integration workflows, is advantageous.
  • Familiarity with big data technologies or distributed computing concepts is helpful but not mandatory.
  • Experience working with cloud storage solutions, data lakes, or data warehouses such as Azure Data Lake Storage, Azure Synapse Analytics, or similar platforms is an added advantage.
  • Basic awareness of Apache Spark, DataFrames, or distributed data processing frameworks will support learning advanced Azure Databricks features.
  • Understanding of file formats such as Parquet, Delta Lake, JSON, Avro, or CSV is beneficial for implementing efficient data engineering solutions.
  • Experience in data analytics, business intelligence, software development, cloud engineering, or database administration is helpful but not required.
  • A strong interest in building modern lakehouse architectures, developing scalable data pipelines, processing large-scale datasets, and preparing for the Microsoft Certified: Azure Databricks Data Engineer Associate Certification (Exam DP-750).

Azure Databricks Data Engineer DP-750 Training in Canada Certification

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Azure Databricks Data Engineer DP-750 Training in Canada FAQ's

This training develops practical expertise in designing and managing data engineering solutions using Azure Databricks. Participants will learn Apache Spark, Delta Lake, data ingestion, ETL and ELT development, Structured Streaming, workflow automation, Unity Catalog, performance optimization, and enterprise data governance for modern cloud-based analytics platforms.

Yes. The course includes extensive hands-on labs where participants build Azure Databricks workspaces, create Spark applications, develop ETL pipelines, implement Delta Lake, process streaming data, optimize Spark jobs, and integrate Azure Databricks with other Azure services using real-world scenarios.

Absolutely. The curriculum covers end-to-end data engineering, including batch data processing, incremental data loading, Structured Streaming, Auto Loader, event-driven pipelines, and lakehouse architectures, enabling participants to build scalable solutions for diverse business requirements.

This course is ideal for Data Engineers, Azure Data Engineers, Apache Spark Developers, ETL Developers, Big Data Engineers, Analytics Engineers, Cloud Data Professionals, and Solution Architects who want to strengthen their expertise in Azure Databricks while preparing for the Microsoft Certified: Azure Databricks Data Engineer Associate Certification (Exam DP-750).

This certification validates your ability to build enterprise-scale data engineering solutions using Azure Databricks and Apache Spark. It prepares professionals for roles such as Azure Databricks Data Engineer, Data Engineer, Apache Spark Developer, Big Data Engineer, Cloud Data Engineer, Analytics Engineer, Data Platform Engineer, and Azure Data Solutions Architect, making them valuable contributors to modern cloud analytics and digital transformation projects.

For course-related queries, email enquiry@multisoftvirtualacademy.com or call +91 8130666206.

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