Who Is This Course For
In today's digital age, data is the new oil. But without proper processing, it's just noise. That's where data engineering comes in.
Our comprehensive data engineering training program is designed for individuals at different stages of their data career journey. Whether you're just starting out or looking to elevate your existing skills, our course caters to a variety of backgrounds and experiences:
Our comprehensive data engineering training program is designed for individuals at different stages of their data career journey. Whether you're just starting out or looking to elevate your existing skills, our course caters to a variety of backgrounds and experiences:
Introduction to Data Engineering
● Definition and scope
● Importance in modern data-driven businesses
Azure Overview
● Introduction to Azure services relevant to data engineering
● Setting up an Azure account and resources
● Definition and scope
● Importance in modern data-driven businesses
Azure Overview
● Introduction to Azure services relevant to data engineering
● Setting up an Azure account and resources
Course Objectives
● Key skills and knowledge to be gained
● Overview of the bootcamp structure and learning outcomes
Learning Path
● Modules, projects, and assessments
● Expectations for hands-on labs and practical assignments
● Key skills and knowledge to be gained
● Overview of the bootcamp structure and learning outcomes
Learning Path
● Modules, projects, and assessments
● Expectations for hands-on labs and practical assignments
Understanding Business Needs
● Identifying stakeholders and their end goals
● Translating business needs into technical requirements
Case Studies
● Real-world examples of data engineering solutions
● Identifying stakeholders and their end goals
● Translating business needs into technical requirements
Case Studies
● Real-world examples of data engineering solutions
Introduction to Data Wrangling
● Definition and significance
● Common data wrangling tasks
● Definition and significance
● Common data wrangling tasks
Understanding Data Storage Options
● Overview of Azure Storage services
Introduction to Data Lakes
● Azure Data Lake Storage (ADLS)
● Setting up and configuring ADLS
● Best practices for data storage and management
● Overview of Azure Storage services
Introduction to Data Lakes
● Azure Data Lake Storage (ADLS)
● Setting up and configuring ADLS
● Best practices for data storage and management
SQL 101
● DBMS, RDBMS and SQL world
Data Modeling Concepts
● Types of data models (relational, dimensional, NoSQL)
● Importance of a well-designed data model
Designing Data Models
● Tools and techniques for data modeling
● Practical examples and case studies
● DBMS, RDBMS and SQL world
Data Modeling Concepts
● Types of data models (relational, dimensional, NoSQL)
● Importance of a well-designed data model
Designing Data Models
● Tools and techniques for data modeling
● Practical examples and case studies
Introduction to Data Orchestration
● Definition and importance
● Key components of orchestration
Azure Data Factory
● Overview and capabilities
● Creating and managing data pipelinesEnhance your workforce's expertise in data engineering to improve project outcomes and drive business success.
● Definition and importance
● Key components of orchestration
Azure Data Factory
● Overview and capabilities
● Creating and managing data pipelinesEnhance your workforce's expertise in data engineering to improve project outcomes and drive business success.
Big Data Analytics Overview
● Key concepts and benefits
Introduction to big data tools in Azure (Spark & Databricks)
● Security in Big Data
● Ensuring data security and compliance
● Best practices and tools for data protection
● Key concepts and benefits
Introduction to big data tools in Azure (Spark & Databricks)
● Security in Big Data
● Ensuring data security and compliance
● Best practices and tools for data protection
Introduction to Data Ingestion
● Understanding data sources and ingestion methods
● Real-time vs. batch ingestion
Azure Data Factory
● Configuring data ingestion pipelines
● Hands-on examples
● Understanding data sources and ingestion methods
● Real-time vs. batch ingestion
Azure Data Factory
● Configuring data ingestion pipelines
● Hands-on examples
Data Transformation Concepts
● Importance and types of data transformation
● ETL vs ELT
Tools for Data Transformation
● Using Azure Data Factory and Databricks for data transformation
● Hands-on labs and exercises
● Importance and types of data transformation
● ETL vs ELT
Tools for Data Transformation
● Using Azure Data Factory and Databricks for data transformation
● Hands-on labs and exercises
Introduction to Reporting
● Importance of reporting in data engineering
● Types of reports and dashboards
Self-Service BI Tools
● Power BI and Azure Analysis Services
● Creating and sharing reports
● Importance of reporting in data engineering
● Types of reports and dashboards
Self-Service BI Tools
● Power BI and Azure Analysis Services
● Creating and sharing reports
Introduction to Lakehouse Architecture
● Definition and key features
● Benefits of lakehouse architecture
Implementing a Lakehouse in Azure
● Tools and best practices
● Case studies and examples
● Definition and key features
● Benefits of lakehouse architecture
Implementing a Lakehouse in Azure
● Tools and best practices
● Case studies and examples
Understanding Medallion Architecture
● Definition and components
● Benefits and use cases
Designing Medallion Architecture in Azure
● Practical steps and tools
● Hands-on exercises
● Definition and components
● Benefits and use cases
Designing Medallion Architecture in Azure
● Practical steps and tools
● Hands-on exercises
Introduction to Databricks
● Overview of Databricks platform and community
● Key features and benefits
Engaging with the Databricks Community
● Resources, forums, and support
● Collaborative projects and contributions
● Overview of Databricks platform and community
● Key features and benefits
Engaging with the Databricks Community
● Resources, forums, and support
● Collaborative projects and contributions
Advanced Orchestration Techniques
● Designing complex data pipelines
● Monitoring and managing pipelines
Hands-on Labs
● Building and deploying pipelines in Azure Data Factory
● Designing complex data pipelines
● Monitoring and managing pipelines
Hands-on Labs
● Building and deploying pipelines in Azure Data Factory
Overview of Orchestration Tools
● Comparison of Azure Data Factory, Dagster, and others
● When to use which tool
Practical Examples
● Implementing orchestration with different tools
● Comparison of Azure Data Factory, Dagster, and others
● When to use which tool
Practical Examples
● Implementing orchestration with different tools
Introduction to Apache Spark
● Key features and benefits
● Spark in Azure (Databricks)
End-to-End Workflow
● Data ingestion, processing, and analysis with Spark
● Hands-on project
● Key features and benefits
● Spark in Azure (Databricks)
End-to-End Workflow
● Data ingestion, processing, and analysis with Spark
● Hands-on project
Overview of Databricks
● Key features and architecture
End-to-End Workflow
● Building a complete data analytics solution in Databricks
● Hands-on project
● Key features and architecture
End-to-End Workflow
● Building a complete data analytics solution in Databricks
● Hands-on project
Key features and benefits
● End-to-End Workflow
● Implementing data quality in your ELT/ETL processes
● End-to-End Workflow
● Implementing data quality in your ELT/ETL processes
Introduction to CI/CD and DataOps
● Key concepts and importance
● Tools and practices in Azure
Implementing CI/CD for Data Pipelines
● Practical steps and tools
● Hands-on examples
● Key concepts and importance
● Tools and practices in Azure
Implementing CI/CD for Data Pipelines
● Practical steps and tools
● Hands-on examples
Key features and benefits
Advanced Topics and Trends
● Machine learning and AI in data engineering
● Emerging technologies and future trends
● Spark/Databricks troubleshooting and optimisation
Group Project
● End-to-end project encompassing all learned skills in a group
● Presentation and feedback Sessions
● Machine learning and AI in data engineering
● Emerging technologies and future trends
● Spark/Databricks troubleshooting and optimisation
Group Project
● End-to-end project encompassing all learned skills in a group
● Presentation and feedback Sessions
