Databricks Workflows and Pipeline Automation Training Course

 Databricks Workflows and Pipeline Automation Training Course 

Introduction 

Databricks Workflows and Pipeline Automation Training Course is designed to equip participants with advanced technical knowledge and practical skills in designing, developing, orchestrating, and automating enterprise data engineering workflows using the Databricks Lakehouse Platform. The course explores modern data pipeline automation, workflow orchestration, Apache Spark processing, Delta Lake architecture, Lakeflow Jobs, Lakeflow Declarative Pipelines, task dependencies, scheduling, and automated data transformation. Participants will gain practical experience building scalable, reliable, and production-ready data pipelines that support real-time analytics, batch processing, business intelligence, and enterprise data management. Emphasis is placed on cloud-native data engineering, workflow monitoring, performance optimization, fault tolerance, and automated data quality management. 

As organizations increasingly adopt cloud computing, artificial intelligence, machine learning, and big data analytics, automated data workflows have become essential for operational efficiency and digital transformation. This training provides hands-on exposure to enterprise workflow development, continuous data integration, automated deployment, pipeline observability, and intelligent job orchestration. Participants will explore practical applications across financial services, healthcare, telecommunications, manufacturing, retail, and logistics, learning how global organizations leverage automated data engineering to improve decision-making, reduce operational costs, and accelerate analytics delivery. By the end of the course, participants will be equipped to implement robust Databricks automation solutions, manage complex data workflows, and develop scalable data engineering architectures aligned with modern industry standards. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand Databricks Workflows and enterprise data pipeline automation.
2. Design scalable data engineering and workflow orchestration architectures.
3. Configure Lakeflow Jobs for automated task execution and scheduling.
4. Develop automated ETL and ELT data processing pipelines.
5. Implement Apache Spark and Delta Lake workflow integration.
6. Manage task dependencies, parameters, triggers, and execution environments.
7. Apply continuous integration and continuous deployment principles.
8. Implement automated data validation and quality monitoring.
9. Optimize workflow performance and resource utilization.
10. Configure pipeline monitoring, logging, and observability.
11. Implement error handling, retries, and fault-tolerant workflows.
12. Integrate cloud storage, databases, and external data sources.
13. Deploy secure, reliable, and production-ready automation solutions.

Organizational Benefits
 

1. Improved enterprise data pipeline automation and operational efficiency.
2. Reduced manual data processing and infrastructure management costs.
3. Faster delivery of business intelligence and analytics.
4. Enhanced data quality, accuracy, and consistency.
5. Improved scalability of enterprise data engineering operations.
6. Increased reliability through automated monitoring and error recovery.
7. Better integration of cloud-based data platforms.
8. Strengthened data governance, security, and compliance.
9. Improved collaboration between data engineers and analytics teams.
10. Accelerated digital transformation and data-driven decision-making.

Target Audiences
 

1. Data Engineers and Big Data Engineers.
2. Data Scientists and Machine Learning Engineers.
3. Cloud Architects and Solutions Architects.
4. Database Administrators and Data Architects.
5. Business Intelligence and Analytics Professionals.
6. DevOps Engineers and Platform Engineers.
7. IT Managers and Enterprise Technology Consultants.
8. Software Developers and Data Platform Administrators.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Databricks Workflows and Pipeline Automation
 

• Overview of Databricks Lakehouse architecture and workflow automation.
• Understanding Lakeflow Jobs and orchestration capabilities.
• Introduction to batch processing and automated data pipelines.
• Exploring workspaces, compute resources, and job configurations.
• Understanding workflow execution and scheduling.
• Case study: Automated enterprise data processing in global financial institutions.

Module 2: Databricks Workflows and Job Orchestration
 

• Creating and configuring multi-task Databricks workflows.
• Managing task dependencies and execution sequences.
• Configuring job parameters, schedules, and triggers.
• Implementing conditional execution and task branching.
• Managing job clusters and serverless compute.
• Case study: Automated transaction processing workflows in international banking.

Module 3: Automated ETL and ELT Pipeline Development
 

• Designing automated data ingestion and transformation workflows.
• Integrating Apache Spark with Delta Lake.
• Developing batch and incremental data pipelines.
• Implementing schema enforcement and schema evolution.
• Automating data cleansing and transformation processes.
• Case study: Retail data integration and inventory automation using enterprise data pipelines.

Module 4: Lakeflow Declarative Pipelines and Data Engineering
 

• Introduction to Lakeflow Declarative Pipelines.
• Developing declarative data transformation workflows.
• Implementing streaming and batch data processing.
• Managing pipeline dependencies and data quality expectations.
• Configuring pipeline execution and continuous updates.
• Case study: Real-time healthcare data processing and analytics automation.

Module 5: Workflow Scheduling, Triggers, and Automation
 

• Configuring scheduled jobs and event-driven execution.
• Implementing file arrival and continuous processing triggers.
• Managing dynamic parameters and reusable workflows.
• Integrating APIs and external data sources.
• Automating recurring enterprise data operations.
• Case study: Automated telecommunications reporting and customer analytics.

Module 6: Pipeline Monitoring, Testing, and Error Management
 

• Monitoring workflow execution and job performance.
• Implementing automated testing and data validation.
• Configuring retries, alerts, and failure notifications.
• Troubleshooting pipeline failures and execution bottlenecks.
• Applying pipeline observability and operational logging.
• Case study: Improving automated supply chain data reliability in global manufacturing.

Module 7: CI/CD, Security, and Deployment Automation
 

• Implementing continuous integration and continuous deployment for Databricks.
• Using Databricks Asset Bundles for deployment management.
• Managing Git integration and version control.
• Configuring access controls, secrets, and service principals.
• Automating development, testing, and production environments.
• Case study: Enterprise deployment automation in multinational technology organizations.

Module 8: Advanced Workflow Optimization and Capstone Project
 

• Optimizing workflow execution and compute resource allocation.
• Implementing scalable and fault-tolerant pipeline architectures.
• Managing workflow costs and performance efficiency.
• Integrating automated pipelines with enterprise analytics platforms.
• Developing an end-to-end production-ready automation solution.
• Case study: Building an automated enterprise Lakehouse pipeline inspired by global e-commerce operations.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using the Databricks Lakehouse Platform.
• Hands-on exercises in workflow orchestration and pipeline automation.
• Real-world enterprise case studies and industry-based scenarios.
• Group assignments and collaborative data engineering projects.
• Practical assessments and end-of-course capstone project.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.