Databricks for Data Scientists and Engineers Training Course

 Databricks for Data Scientists and Engineers Training Course 

Introduction 

Databricks for Data Scientists and Engineers Training Course provides comprehensive practical knowledge and technical expertise in modern data engineering, data science, big data analytics, and cloud-based data processing. The course explores the Databricks Lakehouse Platform, Apache Spark, Delta Lake, machine learning, data pipelines, distributed computing, and advanced analytics. Participants gain practical skills in building scalable data architectures, managing large datasets, developing predictive models, implementing real-time data processing, and transforming raw data into actionable business intelligence. The programme integrates modern technologies, including Python, SQL, PySpark, MLflow, and collaborative notebooks, to strengthen enterprise data capabilities and support data-driven decision-making. 

The training further examines advanced data science workflows, data engineering automation, machine learning lifecycle management, data governance, and cloud-native analytics. Participants learn how to integrate structured and unstructured data, optimize distributed workloads, implement reliable data pipelines, and deploy machine learning solutions using Databricks. Through practical exercises and global industry case studies, the course demonstrates how organizations across finance, healthcare, manufacturing, telecommunications, and retail leverage unified analytics platforms to improve operational efficiency, innovation, and business performance. Emphasis is placed on scalable data solutions, artificial intelligence integration, enterprise analytics, and modern data architecture. 

Course Objectives 

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

1. Understand Databricks architecture and Lakehouse fundamentals.
2. Develop scalable data engineering solutions using Apache Spark.
3. Apply Python, SQL, and PySpark for advanced data analytics.
4. Implement Delta Lake for reliable data storage and management.
5. Build automated ETL and ELT data pipelines.
6. Perform distributed data processing and optimization.
7. Develop machine learning models using Databricks.
8. Apply MLflow for experiment tracking and model management.
9. Integrate real-time data streaming and analytics.
10. Implement data governance, security, and access controls.
11. Optimize cloud-based big data processing workloads.
12. Apply artificial intelligence and predictive analytics techniques.
13. Deploy production-ready data science and engineering solutions.

Organizational Benefits
 

1. Improved enterprise data engineering and analytics capabilities.
2. Reduced data processing costs and operational inefficiencies.
3. Enhanced predictive analytics and business intelligence.
4. Faster machine learning development and deployment.
5. Improved data governance and regulatory compliance.
6. Scalable cloud-based data infrastructure.
7. Better collaboration between data scientists and engineers.
8. Increased automation of enterprise data workflows.
9. Improved data quality and reliability.
10. Stronger innovation and data-driven decision-making.

Target Audiences
 

1. Data scientists and machine learning practitioners.
2. Data engineers and big data developers.
3. Business intelligence and analytics professionals.
4. Cloud architects and solution architects.
5. Database administrators and data analysts.
6. Artificial intelligence and machine learning engineers.
7. Software developers working with data-intensive applications.
8. IT managers and enterprise data professionals.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Databricks and Lakehouse Architecture
 

• Databricks platform overview and core capabilities.
• Lakehouse architecture and data management principles.
• Apache Spark fundamentals and distributed computing.
• Databricks workspaces, clusters, and notebooks.
• Collaborative analytics and cloud integration.
• Case study: Uber's large-scale data processing and analytics infrastructure.

Module 2: Data Engineering with Apache Spark and PySpark
 

• Spark DataFrames, RDDs, and Spark SQL.
• PySpark transformations and data actions.
• Distributed data processing and partitioning.
• Performance optimization and caching techniques.
• Structured and unstructured data processing.
• Case study: Netflix's distributed data engineering and recommendation analytics.

Module 3: Delta Lake and Modern Data Management
 

• Delta Lake architecture and transaction management.
• ACID transactions and schema enforcement.
• Time travel and data versioning.
• Data quality monitoring and validation.
• Medallion architecture: Bronze, Silver, and Gold layers.
• Case study: Walmart's enterprise-scale data management and analytics.

Module 4: Data Science and Advanced Analytics
 

• Exploratory data analysis using Databricks.
• Statistical analysis and feature engineering.
• Data visualization and analytical notebooks.
• Python libraries for advanced data science.
• Predictive analytics and statistical modelling.
• Case study: Healthcare analytics for disease prediction and patient outcomes.

Module 5: Machine Learning and MLflow
 

• Machine learning workflows on Databricks.
• Model development and hyperparameter tuning.
• MLflow experiment tracking and model registry.
• Model evaluation and performance monitoring.
• Machine learning deployment and lifecycle management.
• Case study: Financial institutions applying machine learning for fraud detection.

Module 6: Data Pipelines and Real-Time Streaming
 

• Automated ETL and ELT pipeline development.
• Structured Streaming and real-time analytics.
• Data ingestion and transformation workflows.
• Workflow orchestration and job scheduling.
• Monitoring and troubleshooting data pipelines.
• Case study: Uber's real-time data processing for transportation analytics.

Module 7: Cloud Integration, Security and Governance
 

• Databricks integration with cloud storage platforms.
• Unity Catalog and centralized data governance.
• Role-based access control and data security.
• Data lineage, auditing, and compliance.
• Enterprise data sharing and collaboration.
• Case study: Microsoft Azure enterprise analytics and governed data environments.

Module 8: Advanced Databricks Applications and Capstone Project
 

• Advanced Lakehouse analytics and optimization.
• Generative artificial intelligence and large language model integration.
• Building end-to-end data science workflows.
• Production deployment and performance monitoring.
• Enterprise data architecture and solution implementation.
• Case study: Amazon's recommendation systems and large-scale predictive analytics.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Databricks workspaces.
• Hands-on exercises involving Python, SQL, and PySpark.
• Real-world data engineering and machine learning projects.
• Group assignments and collaborative problem-solving.
• Global industry case studies and capstone project presentations.
• Practical assessments and instructor feedback.

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.