Mastering Azure Databricks for Modern Data Engineering
You're tired of fragmented pipelines, unreliable data quality, and systems that break under real-world load. The pressure to deliver timely, accurate insights is rising, but legacy tools and unclear architectures keep you stuck in reactive firefighting mode, not strategic innovation. Every delayed insight undermines stakeholder trust. Every unoptimised job increases cloud spend. And every day without a scalable data engineering framework widens the gap between your current state and the modern data stack your competitors have already adopted. Mastering Azure Databricks for Modern Data Engineering is the definitive roadmap to transform how you design, build, and optimise data platforms on Azure. This course isn’t theory-it’s the exact blueprint used by elite data engineers to deliver robust, high-performance data architectures that power enterprise AI and analytics at scale. You’ll go from concept to production-grade architecture in 30 days, with a fully documented, modular data pipeline ready for board-level presentation and immediate deployment. A recent learner, Priya M., Senior Data Engineer at a global logistics firm, completed the course while restructuring her company's legacy ETL system. Within four weeks, she deployed a Delta Lake-based pipeline that reduced end-to-end latency by 78%, cut compute costs by 41%, and earned her a direct sponsorship from the CDO for a promotion. This course eliminates the guesswork, vendor noise, and outdated patterns that slow your progress. Here’s how this course is structured to help you get there.Course Format & Delivery Details Designed for working professionals, Mastering Azure Databricks for Modern Data Engineering is a self-paced course with immediate online access. Begin learning the moment you enroll-no waiting for enrollment windows or fixed start dates. Most learners complete the core curriculum in 25–30 hours, with tangible results visible within the first week. You’ll deploy your first optimised pipeline by Day 7, and your full architecture blueprint within 30 days. What You Get
- Self-paced, on-demand access-learn anytime, anywhere, with no mandatory schedules or deadlines
- Lifetime access to all course materials, including all future updates at no additional cost
- 24/7 global access across all devices, with full mobile compatibility for learning on the go
- Structured progression paths with progress tracking and milestone checkpoints to reinforce retention
- Dedicated instructor support through curated guidance and real-world use case analysis
- A professional Certificate of Completion issued by The Art of Service, a globally recognised credential trusted by professionals in over 160 countries
Zero-Risk Enrollment Guarantee
We understand that your time is valuable and your goals are serious. That’s why we offer a no-questions-asked, satisfied or refunded guarantee. If the course doesn’t deliver clear, measurable value within your first two modules, simply request a full refund. Clarity Without Hidden Costs
Pricing is straightforward with no hidden fees, subscriptions, or renewal charges. The one-time fee includes everything: curriculum, implementation frameworks, performance benchmarks, and certification. Secure checkout accepts Visa, Mastercard, and PayPal-ensuring fast, trusted, and globally accessible enrollment. You’ll Receive Full Access in Two Steps
Upon enrollment, you’ll receive a confirmation email. Your detailed access instructions and learning portal credentials will be sent separately once your course materials are prepared-ensuring a seamless onboarding experience. This Course Works-Even If…
- You’ve struggled with Azure Databricks before due to poorly structured tutorials or missing real-world context
- Your current role doesn’t yet involve Databricks, but you’re preparing for a high-impact data engineering or cloud analytics position
- You’re transitioning from on-premise ETL tools like SSIS or Informatica and need a clear, modern migration path
- You’re already using Databricks but lack confidence in optimising cost, performance, or governance
- You're time-constrained and need maximum ROI per learning hour
With detailed role-specific implementation guides, guided architecture decisions, and hands-on project templates, this course delivers actionable clarity-no matter your starting point.
Module 1: Foundations of Modern Data Engineering on Azure - Introduction to the modern data stack and evolving enterprise needs
- Understanding the role of data engineering in AI and analytics maturity
- Comparing legacy ETL vs. cloud-native data architectures
- Azure Databricks as a core component of the intelligent data platform
- Overview of Azure cloud services integrated with Databricks
- Key principles of scalability, reliability, and maintainability
- The shift from batch to real-time processing paradigms
- Data ownership, lineage, and stewardship in distributed environments
- Common pain points in data engineering and how Databricks resolves them
- Architectural maturity model for data platforms on Azure
Module 2: Azure Databricks Core Architecture and Setup - Understanding Databricks workspaces and deployment models
- Setting up your Azure Databricks workspace with secure networking
- Configuring managed vs. customer-managed identities
- Implementing role-based access control (RBAC) for teams
- Integrating with Azure Key Vault for secret management
- Virtual network peering and private endpoint configuration
- Best practices for workspace naming, tagging, and governance
- Cluster architecture: job, interactive, and all-purpose clusters
- Autoscaling logic and cluster optimisation strategies
- Using cluster policies to enforce standards across teams
Module 3: Delta Lake Fundamentals and Data Reliability - Why Delta Lake is essential for modern data engineering
- Creating and managing Delta tables with ACID transactions
- Schema evolution and enforcement in production pipelines
- Time travel and data versioning for audit and recovery
- Optimising file sizes using Z-Ordering and compaction
- Managing metadata and transaction logs effectively
- Implementing data quality checks with expectations
- Handling CDC (change data capture) with SCD Type 2 patterns
- Building reliable ingestion layers from source systems
- Managing soft deletes and data masking in Delta
Module 4: Ingestion Patterns and Source Integration - Batch ingestion from Azure Blob Storage and Azure Data Lake Gen2
- Streaming ingestion using Apache Kafka and Azure Event Hubs
- Extracting data from SQL Server, Oracle, and PostgreSQL
- Using Databricks connectors for SAP, Salesforce, and Dynamics
- Working with semi-structured data: JSON, XML, Parquet
- Handling schema drift during ingestion
- Designing idempotent ingestion pipelines
- Checkpoint management in streaming workloads
- Partitioning strategies for scalable reads and writes
- Monitoring ingestion latency and backpressure signals
Module 5: Unified Batch and Streaming with Structured Streaming - Core concepts of event time, processing time, and watermarks
- Building stateful stream processing applications
- Handling late-arriving data with windowed aggregations
- Using foreachBatch for custom sink operations
- Integrating streaming with Delta Lake for upserts
- Monitoring stream health and processing metrics
- Scaling streaming jobs across multiple executors
- Designing fault-tolerant streaming architectures
- Implementing watermark propagation across stages
- Testing streaming logic with synthetic data generators
Module 6: Data Transformation and Pipeline Design - Defining transformation layers: raw, bronze, silver, gold
- Creating reusable transformation functions with Python and SQL
- Encapsulating logic with Databricks notebooks and workflows
- Managing dependencies between pipeline stages
- Building dynamic pipelines using parameterisation
- Versioning pipeline code with Git integration
- Using widgets for configuration and testing
- Logging and auditing transformation steps for compliance
- Handling errors and retries with structured exception handling
- Designing for pipeline reprocessing and backfills
Module 7: Performance Optimisation and Cost Efficiency - Understanding Databricks pricing models: DBUs and compute tiers
- Analysing job cost breakdown and identifying hotspots
- Optimising executor memory and core allocation
- Monitoring cluster utilisation and idle time
- Choosing between Photon and non-Photon runtimes
- Improving query performance with caching and materialisation
- Using EXPLAIN plans to identify bottlenecks
- Tuning shuffle partitions for large-scale joins
- Leveraging caching strategies with managed and unmanaged tables
- Automating cost alerts with Azure Monitor integration
Module 8: Workflow Orchestration with Databricks Workflows - Creating multi-task job workflows for end-to-end pipelines
- Scheduling jobs with precise recurrence and time zones
- Setting up email and Slack notifications for job status
- Configuring job retries and failure thresholds
- Using task dependencies to model complex workflows
- Passing values between tasks using output references
- Monitoring workflow run history and performance trends
- Integrating with Azure Logic Apps for external coordination
- Synchronising workflows with metadata-driven triggers
- Ensuring workflow idempotency and re-runnability
Module 9: Data Governance and Compliance - Implementing data classification and sensitivity labelling
- Setting up data access reviews and entitlement reporting
- Using Unity Catalog for centralised governance
- Managing metastores and sharing across workspaces
- Enforcing column-level and row-level security
- Audit logging and data access monitoring
- Integrating with Azure Purview for enterprise metadata
- Meeting GDPR, HIPAA, and SOX compliance requirements
- Documenting data lineage across pipeline stages
- Creating data dictionaries and stakeholder-facing catalogs
Module 10: Advanced Analytics and Machine Learning Integration - Preparing clean, model-ready datasets from silver and gold tables
- Feature engineering with Scikit-learn and MLflow
- Versioning datasets and models together
- Building automated retraining pipelines
- Implementing batch scoring at scale
- Deploying models with Databricks Model Serving
- Monitoring model drift and data quality decay
- Using AutoML for rapid prototyping
- Integrating with Azure Machine Learning workspaces
- Creating unified workflows for analytics and ML teams
Module 11: Productionisation and CI/CD - Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Introduction to the modern data stack and evolving enterprise needs
- Understanding the role of data engineering in AI and analytics maturity
- Comparing legacy ETL vs. cloud-native data architectures
- Azure Databricks as a core component of the intelligent data platform
- Overview of Azure cloud services integrated with Databricks
- Key principles of scalability, reliability, and maintainability
- The shift from batch to real-time processing paradigms
- Data ownership, lineage, and stewardship in distributed environments
- Common pain points in data engineering and how Databricks resolves them
- Architectural maturity model for data platforms on Azure
Module 2: Azure Databricks Core Architecture and Setup - Understanding Databricks workspaces and deployment models
- Setting up your Azure Databricks workspace with secure networking
- Configuring managed vs. customer-managed identities
- Implementing role-based access control (RBAC) for teams
- Integrating with Azure Key Vault for secret management
- Virtual network peering and private endpoint configuration
- Best practices for workspace naming, tagging, and governance
- Cluster architecture: job, interactive, and all-purpose clusters
- Autoscaling logic and cluster optimisation strategies
- Using cluster policies to enforce standards across teams
Module 3: Delta Lake Fundamentals and Data Reliability - Why Delta Lake is essential for modern data engineering
- Creating and managing Delta tables with ACID transactions
- Schema evolution and enforcement in production pipelines
- Time travel and data versioning for audit and recovery
- Optimising file sizes using Z-Ordering and compaction
- Managing metadata and transaction logs effectively
- Implementing data quality checks with expectations
- Handling CDC (change data capture) with SCD Type 2 patterns
- Building reliable ingestion layers from source systems
- Managing soft deletes and data masking in Delta
Module 4: Ingestion Patterns and Source Integration - Batch ingestion from Azure Blob Storage and Azure Data Lake Gen2
- Streaming ingestion using Apache Kafka and Azure Event Hubs
- Extracting data from SQL Server, Oracle, and PostgreSQL
- Using Databricks connectors for SAP, Salesforce, and Dynamics
- Working with semi-structured data: JSON, XML, Parquet
- Handling schema drift during ingestion
- Designing idempotent ingestion pipelines
- Checkpoint management in streaming workloads
- Partitioning strategies for scalable reads and writes
- Monitoring ingestion latency and backpressure signals
Module 5: Unified Batch and Streaming with Structured Streaming - Core concepts of event time, processing time, and watermarks
- Building stateful stream processing applications
- Handling late-arriving data with windowed aggregations
- Using foreachBatch for custom sink operations
- Integrating streaming with Delta Lake for upserts
- Monitoring stream health and processing metrics
- Scaling streaming jobs across multiple executors
- Designing fault-tolerant streaming architectures
- Implementing watermark propagation across stages
- Testing streaming logic with synthetic data generators
Module 6: Data Transformation and Pipeline Design - Defining transformation layers: raw, bronze, silver, gold
- Creating reusable transformation functions with Python and SQL
- Encapsulating logic with Databricks notebooks and workflows
- Managing dependencies between pipeline stages
- Building dynamic pipelines using parameterisation
- Versioning pipeline code with Git integration
- Using widgets for configuration and testing
- Logging and auditing transformation steps for compliance
- Handling errors and retries with structured exception handling
- Designing for pipeline reprocessing and backfills
Module 7: Performance Optimisation and Cost Efficiency - Understanding Databricks pricing models: DBUs and compute tiers
- Analysing job cost breakdown and identifying hotspots
- Optimising executor memory and core allocation
- Monitoring cluster utilisation and idle time
- Choosing between Photon and non-Photon runtimes
- Improving query performance with caching and materialisation
- Using EXPLAIN plans to identify bottlenecks
- Tuning shuffle partitions for large-scale joins
- Leveraging caching strategies with managed and unmanaged tables
- Automating cost alerts with Azure Monitor integration
Module 8: Workflow Orchestration with Databricks Workflows - Creating multi-task job workflows for end-to-end pipelines
- Scheduling jobs with precise recurrence and time zones
- Setting up email and Slack notifications for job status
- Configuring job retries and failure thresholds
- Using task dependencies to model complex workflows
- Passing values between tasks using output references
- Monitoring workflow run history and performance trends
- Integrating with Azure Logic Apps for external coordination
- Synchronising workflows with metadata-driven triggers
- Ensuring workflow idempotency and re-runnability
Module 9: Data Governance and Compliance - Implementing data classification and sensitivity labelling
- Setting up data access reviews and entitlement reporting
- Using Unity Catalog for centralised governance
- Managing metastores and sharing across workspaces
- Enforcing column-level and row-level security
- Audit logging and data access monitoring
- Integrating with Azure Purview for enterprise metadata
- Meeting GDPR, HIPAA, and SOX compliance requirements
- Documenting data lineage across pipeline stages
- Creating data dictionaries and stakeholder-facing catalogs
Module 10: Advanced Analytics and Machine Learning Integration - Preparing clean, model-ready datasets from silver and gold tables
- Feature engineering with Scikit-learn and MLflow
- Versioning datasets and models together
- Building automated retraining pipelines
- Implementing batch scoring at scale
- Deploying models with Databricks Model Serving
- Monitoring model drift and data quality decay
- Using AutoML for rapid prototyping
- Integrating with Azure Machine Learning workspaces
- Creating unified workflows for analytics and ML teams
Module 11: Productionisation and CI/CD - Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Why Delta Lake is essential for modern data engineering
- Creating and managing Delta tables with ACID transactions
- Schema evolution and enforcement in production pipelines
- Time travel and data versioning for audit and recovery
- Optimising file sizes using Z-Ordering and compaction
- Managing metadata and transaction logs effectively
- Implementing data quality checks with expectations
- Handling CDC (change data capture) with SCD Type 2 patterns
- Building reliable ingestion layers from source systems
- Managing soft deletes and data masking in Delta
Module 4: Ingestion Patterns and Source Integration - Batch ingestion from Azure Blob Storage and Azure Data Lake Gen2
- Streaming ingestion using Apache Kafka and Azure Event Hubs
- Extracting data from SQL Server, Oracle, and PostgreSQL
- Using Databricks connectors for SAP, Salesforce, and Dynamics
- Working with semi-structured data: JSON, XML, Parquet
- Handling schema drift during ingestion
- Designing idempotent ingestion pipelines
- Checkpoint management in streaming workloads
- Partitioning strategies for scalable reads and writes
- Monitoring ingestion latency and backpressure signals
Module 5: Unified Batch and Streaming with Structured Streaming - Core concepts of event time, processing time, and watermarks
- Building stateful stream processing applications
- Handling late-arriving data with windowed aggregations
- Using foreachBatch for custom sink operations
- Integrating streaming with Delta Lake for upserts
- Monitoring stream health and processing metrics
- Scaling streaming jobs across multiple executors
- Designing fault-tolerant streaming architectures
- Implementing watermark propagation across stages
- Testing streaming logic with synthetic data generators
Module 6: Data Transformation and Pipeline Design - Defining transformation layers: raw, bronze, silver, gold
- Creating reusable transformation functions with Python and SQL
- Encapsulating logic with Databricks notebooks and workflows
- Managing dependencies between pipeline stages
- Building dynamic pipelines using parameterisation
- Versioning pipeline code with Git integration
- Using widgets for configuration and testing
- Logging and auditing transformation steps for compliance
- Handling errors and retries with structured exception handling
- Designing for pipeline reprocessing and backfills
Module 7: Performance Optimisation and Cost Efficiency - Understanding Databricks pricing models: DBUs and compute tiers
- Analysing job cost breakdown and identifying hotspots
- Optimising executor memory and core allocation
- Monitoring cluster utilisation and idle time
- Choosing between Photon and non-Photon runtimes
- Improving query performance with caching and materialisation
- Using EXPLAIN plans to identify bottlenecks
- Tuning shuffle partitions for large-scale joins
- Leveraging caching strategies with managed and unmanaged tables
- Automating cost alerts with Azure Monitor integration
Module 8: Workflow Orchestration with Databricks Workflows - Creating multi-task job workflows for end-to-end pipelines
- Scheduling jobs with precise recurrence and time zones
- Setting up email and Slack notifications for job status
- Configuring job retries and failure thresholds
- Using task dependencies to model complex workflows
- Passing values between tasks using output references
- Monitoring workflow run history and performance trends
- Integrating with Azure Logic Apps for external coordination
- Synchronising workflows with metadata-driven triggers
- Ensuring workflow idempotency and re-runnability
Module 9: Data Governance and Compliance - Implementing data classification and sensitivity labelling
- Setting up data access reviews and entitlement reporting
- Using Unity Catalog for centralised governance
- Managing metastores and sharing across workspaces
- Enforcing column-level and row-level security
- Audit logging and data access monitoring
- Integrating with Azure Purview for enterprise metadata
- Meeting GDPR, HIPAA, and SOX compliance requirements
- Documenting data lineage across pipeline stages
- Creating data dictionaries and stakeholder-facing catalogs
Module 10: Advanced Analytics and Machine Learning Integration - Preparing clean, model-ready datasets from silver and gold tables
- Feature engineering with Scikit-learn and MLflow
- Versioning datasets and models together
- Building automated retraining pipelines
- Implementing batch scoring at scale
- Deploying models with Databricks Model Serving
- Monitoring model drift and data quality decay
- Using AutoML for rapid prototyping
- Integrating with Azure Machine Learning workspaces
- Creating unified workflows for analytics and ML teams
Module 11: Productionisation and CI/CD - Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Core concepts of event time, processing time, and watermarks
- Building stateful stream processing applications
- Handling late-arriving data with windowed aggregations
- Using foreachBatch for custom sink operations
- Integrating streaming with Delta Lake for upserts
- Monitoring stream health and processing metrics
- Scaling streaming jobs across multiple executors
- Designing fault-tolerant streaming architectures
- Implementing watermark propagation across stages
- Testing streaming logic with synthetic data generators
Module 6: Data Transformation and Pipeline Design - Defining transformation layers: raw, bronze, silver, gold
- Creating reusable transformation functions with Python and SQL
- Encapsulating logic with Databricks notebooks and workflows
- Managing dependencies between pipeline stages
- Building dynamic pipelines using parameterisation
- Versioning pipeline code with Git integration
- Using widgets for configuration and testing
- Logging and auditing transformation steps for compliance
- Handling errors and retries with structured exception handling
- Designing for pipeline reprocessing and backfills
Module 7: Performance Optimisation and Cost Efficiency - Understanding Databricks pricing models: DBUs and compute tiers
- Analysing job cost breakdown and identifying hotspots
- Optimising executor memory and core allocation
- Monitoring cluster utilisation and idle time
- Choosing between Photon and non-Photon runtimes
- Improving query performance with caching and materialisation
- Using EXPLAIN plans to identify bottlenecks
- Tuning shuffle partitions for large-scale joins
- Leveraging caching strategies with managed and unmanaged tables
- Automating cost alerts with Azure Monitor integration
Module 8: Workflow Orchestration with Databricks Workflows - Creating multi-task job workflows for end-to-end pipelines
- Scheduling jobs with precise recurrence and time zones
- Setting up email and Slack notifications for job status
- Configuring job retries and failure thresholds
- Using task dependencies to model complex workflows
- Passing values between tasks using output references
- Monitoring workflow run history and performance trends
- Integrating with Azure Logic Apps for external coordination
- Synchronising workflows with metadata-driven triggers
- Ensuring workflow idempotency and re-runnability
Module 9: Data Governance and Compliance - Implementing data classification and sensitivity labelling
- Setting up data access reviews and entitlement reporting
- Using Unity Catalog for centralised governance
- Managing metastores and sharing across workspaces
- Enforcing column-level and row-level security
- Audit logging and data access monitoring
- Integrating with Azure Purview for enterprise metadata
- Meeting GDPR, HIPAA, and SOX compliance requirements
- Documenting data lineage across pipeline stages
- Creating data dictionaries and stakeholder-facing catalogs
Module 10: Advanced Analytics and Machine Learning Integration - Preparing clean, model-ready datasets from silver and gold tables
- Feature engineering with Scikit-learn and MLflow
- Versioning datasets and models together
- Building automated retraining pipelines
- Implementing batch scoring at scale
- Deploying models with Databricks Model Serving
- Monitoring model drift and data quality decay
- Using AutoML for rapid prototyping
- Integrating with Azure Machine Learning workspaces
- Creating unified workflows for analytics and ML teams
Module 11: Productionisation and CI/CD - Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Understanding Databricks pricing models: DBUs and compute tiers
- Analysing job cost breakdown and identifying hotspots
- Optimising executor memory and core allocation
- Monitoring cluster utilisation and idle time
- Choosing between Photon and non-Photon runtimes
- Improving query performance with caching and materialisation
- Using EXPLAIN plans to identify bottlenecks
- Tuning shuffle partitions for large-scale joins
- Leveraging caching strategies with managed and unmanaged tables
- Automating cost alerts with Azure Monitor integration
Module 8: Workflow Orchestration with Databricks Workflows - Creating multi-task job workflows for end-to-end pipelines
- Scheduling jobs with precise recurrence and time zones
- Setting up email and Slack notifications for job status
- Configuring job retries and failure thresholds
- Using task dependencies to model complex workflows
- Passing values between tasks using output references
- Monitoring workflow run history and performance trends
- Integrating with Azure Logic Apps for external coordination
- Synchronising workflows with metadata-driven triggers
- Ensuring workflow idempotency and re-runnability
Module 9: Data Governance and Compliance - Implementing data classification and sensitivity labelling
- Setting up data access reviews and entitlement reporting
- Using Unity Catalog for centralised governance
- Managing metastores and sharing across workspaces
- Enforcing column-level and row-level security
- Audit logging and data access monitoring
- Integrating with Azure Purview for enterprise metadata
- Meeting GDPR, HIPAA, and SOX compliance requirements
- Documenting data lineage across pipeline stages
- Creating data dictionaries and stakeholder-facing catalogs
Module 10: Advanced Analytics and Machine Learning Integration - Preparing clean, model-ready datasets from silver and gold tables
- Feature engineering with Scikit-learn and MLflow
- Versioning datasets and models together
- Building automated retraining pipelines
- Implementing batch scoring at scale
- Deploying models with Databricks Model Serving
- Monitoring model drift and data quality decay
- Using AutoML for rapid prototyping
- Integrating with Azure Machine Learning workspaces
- Creating unified workflows for analytics and ML teams
Module 11: Productionisation and CI/CD - Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Implementing data classification and sensitivity labelling
- Setting up data access reviews and entitlement reporting
- Using Unity Catalog for centralised governance
- Managing metastores and sharing across workspaces
- Enforcing column-level and row-level security
- Audit logging and data access monitoring
- Integrating with Azure Purview for enterprise metadata
- Meeting GDPR, HIPAA, and SOX compliance requirements
- Documenting data lineage across pipeline stages
- Creating data dictionaries and stakeholder-facing catalogs
Module 10: Advanced Analytics and Machine Learning Integration - Preparing clean, model-ready datasets from silver and gold tables
- Feature engineering with Scikit-learn and MLflow
- Versioning datasets and models together
- Building automated retraining pipelines
- Implementing batch scoring at scale
- Deploying models with Databricks Model Serving
- Monitoring model drift and data quality decay
- Using AutoML for rapid prototyping
- Integrating with Azure Machine Learning workspaces
- Creating unified workflows for analytics and ML teams
Module 11: Productionisation and CI/CD - Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Setting up development, staging, and production environments
- Managing configurations with environment variables
- Using Databricks CLI for deployment automation
- Integrating with GitHub Actions for CI/CD pipelines
- Automated testing of data pipelines
- Validating schema and data quality pre-deployment
- Blue-green deployment strategies for zero downtime
- Infrastructure as Code using Terraform for Databricks
- Managing workspace-level configuration as code
- Rollback procedures for failed deployments
Module 12: Monitoring, Alerting, and Observability - Configuring job and cluster-level logging
- Streaming logs to Azure Log Analytics
- Setting up custom dashboards with Kusto queries
- Defining critical metrics: job duration, throughput, errors
- Creating alerts for SLA breaches and exceptions
- Using Databricks System Tables for observability
- Monitoring cluster health and node failures
- Analysing slow queries and long-running tasks
- Implementing distributed tracing for pipeline stages
- Creating runbooks for common operational issues
Module 13: Scalability and High Availability Patterns - Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- Designing pipelines for petabyte-scale data
- Sharding strategies for parallel processing
- Handling peak loads with dynamic cluster scaling
- Replicating data across regions for disaster recovery
- Testing failover scenarios with controlled outages
- Using geo-redundant storage for resilience
- Managing metadata consistency across regions
- Designing for multi-workspace collaboration
- Load balancing across multiple pipelines
- Planning for exponential data growth
Module 14: Real-World Project: End-to-End Pipeline Implementation - Defining business requirements for a global retail analytics platform
- Designing source-to-consumer architecture
- Setting up secure Databricks workspace and networking
- Ingesting sales data from cloud storage and streaming sources
- Building bronze, silver, and gold layer transformations
- Implementing data quality rules and exception handling
- Optimising performance using clustering and partitioning
- Creating scheduled workflows with dependency management
- Deploying pipeline via CI/CD to production
- Configuring monitoring, alerts, and dashboards
- Generating lineage reports and governance documentation
- Preparing executive summary and technical handover
Module 15: Career Advancement and Certification - How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service
- How to showcase your Databricks project on LinkedIn and resumes
- Translating technical skills into business impact statements
- Preparing for data engineering interview questions
- Navigating career paths: from engineer to architect to lead
- Building a professional portfolio with real implementations
- Networking with the Azure and Databricks community
- Leveraging the Certificate of Completion for visibility
- Using certification to negotiate higher compensation
- Accessing exclusive job boards and alumni networks
- Claiming your Certificate of Completion issued by The Art of Service