Azure Data Bricks Training

About Course

Azure Databricks Training provides a comprehensive understanding of Azure Databricks, a unified analytics platform for big data and AI. This training equips individuals with the skills to process large datasets, build data pipelines, and perform advanced analytics using Databricks notebooks. Participants learn to leverage the power of Apache Spark for data processing and machine learning tasks within the Azure ecosystem. With hands-on exercises and real-world use cases, this training enables data engineers, data scientists, and analysts to harness the full potential of Azure Databricks, improving data-driven decision-making and driving innovation in organizations’ data projects.

Course Content

Module 1: Introduction to Azure Databricks

Introduction to big data and data engineering
Overview of Azure Databricks
Setting up an Azure Databricks workspace
Creating clusters and notebooks

Module 2: Data Ingestion

Module 3: Data Transformation and Processing

Module 4: Machine Learning with Azure Databricks

Module 5: Data Visualization and Reporting

Module 6: Data Security and Compliance

Module 7: Performance Optimization

Module 8: Real-world Use Cases and Case Studies

Module 9: Advanced Topics

Module 10: Capstone Project and Certification

About Course

What Will You Learn?

Course Content

Module 1: Introduction to Azure Databricks

Introduction to big data and data engineering

Overview of Azure Databricks

Setting up an Azure Databricks workspace

Creating clusters and notebooks

Module 2: Data Ingestion

Data sources and formats

Batch and real-time data ingestion

Data ingestion from Azure Data Lake Storage, Azure Blob Storage, and other sources

ETL (Extract, Transform, Load) processes

Module 3: Data Transformation and Processing

Using Databricks notebooks for data transformation

Data preparation and cleaning

Data transformation with Spark SQL and Python

Applying data transformations at scale

Module 4: Machine Learning with Azure Databricks

Introduction to machine learning on Databricks

MLflow for managing machine learning workflows

Model development and deployment

Hyperparameter tuning and model tracking

Module 5: Data Visualization and Reporting

Data visualization using libraries like Matplotlib, Seaborn, and Plotly

Reporting and dashboards with Databricks and Power BI

Best practices for creating effective data visualizations

Module 6: Data Security and Compliance

Security features in Azure Databricks

Role-based access control (RBAC)

Data encryption and compliance

Monitoring and auditing

Module 7: Performance Optimization

Optimizing Spark jobs for performance

Cluster configuration and scaling

Caching and checkpointing

Query optimization

Module 8: Real-world Use Cases and Case Studies

Industry-specific use cases (e.g., finance, healthcare, e-commerce)

Customer success stories

Hands-on projects and case studies

Module 9: Advanced Topics

Streaming data processing with Databricks

Integration with Azure services (e.g., Azure Synapse Analytics, Azure Machine Learning)

Containerization and orchestration with Azure Kubernetes Service (AKS)

Module 10: Capstone Project and Certification

Final project development

Presentation of projects

Certification exam or assessment