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Azure Data Factory

Information Technology > Data Integration

Description

Microsoft Azure Data Factory is a cloud-based data integration service that enables the creation, scheduling, and orchestration of data pipelines to move and transform data from various sources to destinations. It provides a user-friendly interface for designing workflows, supports a wide range of data sources, and offers built-in activities for data movement and transformation. With Azure Data Factory, users can automate data workflows, monitor pipeline performance, and integrate with other Azure services for comprehensive data management. It is ideal for building scalable, reliable, and efficient data integration solutions, making it a powerful tool for businesses looking to streamline their data processes and gain actionable insights.

Stack

Microsoft

Expected Behaviors

✎
LEVEL 1

Fundamental Awareness

At the fundamental awareness level, individuals are expected to understand the basic concepts and purposes of Azure Data Factory, navigate its interface, create simple data pipelines, and recognize different data sources and sinks.

🌱
LEVEL 2

Novice

Novices can configure linked services, manage datasets, implement basic data movement activities, and monitor and troubleshoot pipelines. They have a foundational understanding of how to set up and manage data workflows in Azure Data Factory.

🌍
LEVEL 3

Intermediate

Intermediate users can orchestrate complex data workflows using control flow activities, perform data transformations, utilize parameters and variables, and integrate Azure Data Factory with other Azure services. They are proficient in managing more sophisticated data operations.

⭐
LEVEL 4

Advanced

Advanced practitioners optimize pipeline performance, implement complex data transformations, manage execution triggers and schedules, and apply security best practices. They are adept at handling intricate data integration tasks and ensuring efficient and secure data processing.

🏆
LEVEL 5

Expert

Experts design and implement comprehensive data integration solutions, automate deployment and version control, implement advanced monitoring and alerting strategies, and leverage custom activities for extensibility. They possess deep expertise in creating robust, scalable, and maintainable data workflows.

Micro Skills

✎
LEVEL 1

Fundamental Awareness

Defining data integration and ETL processes
Identifying scenarios where Azure Data Factory is beneficial
Exploring industry use cases for Azure Data Factory
Comparing Azure Data Factory with other data integration tools
Accessing Azure Data Factory via the Azure portal
Understanding the layout of the Azure Data Factory UI
Locating key features and components in the interface
Customizing the workspace for better usability
Setting up a new data pipeline project
Adding activities to the pipeline
Configuring activity properties
Running and validating the pipeline
Identifying supported data sources
Identifying supported data sinks
Configuring connections to data sources
Configuring connections to data sinks
🌱
LEVEL 2

Novice

Understanding the concept of linked services
Creating a linked service to an Azure Blob Storage
Creating a linked service to an Azure SQL Database
Testing and validating linked services
Managing linked service credentials securely
Understanding the concept of datasets
Creating a dataset for Azure Blob Storage
Creating a dataset for Azure SQL Database
Configuring dataset properties
Managing dataset schema and structure
Understanding data movement activities
Configuring a copy activity
Setting up source and sink properties
Handling data format conversions
Monitoring data movement activity performance
Accessing pipeline run history
Interpreting pipeline run logs
Identifying common pipeline errors
Using Azure Monitor for pipeline diagnostics
Implementing retry policies for failed activities
🌍
LEVEL 3

Intermediate

Understanding different types of control flow activities
Implementing conditional logic with If Condition activity
Using ForEach activity to iterate over a collection
Implementing Wait activity for time-based control
Utilizing Execute Pipeline activity for modular workflows
Understanding the purpose of data transformation activities
Using Copy Data activity for basic transformations
Implementing Data Flow activity for complex transformations
Configuring mapping data flows
Using built-in transformations like Join, Aggregate, and Filter
Defining parameters at pipeline and activity levels
Passing parameter values during pipeline execution
Using system variables for dynamic content
Implementing expressions to manipulate parameter values
Debugging pipelines with parameterized inputs
Connecting to Azure Blob Storage as a data source/sink
Integrating with Azure SQL Database for data operations
Using Azure Key Vault for secure credential management
Implementing Event Grid for event-driven data processing
Connecting to Azure Data Lake Storage for big data scenarios
⭐
LEVEL 4

Advanced

Identifying and resolving data pipeline bottlenecks
Implementing parallelism in data activities
Using staging storage for intermediate data
Configuring data partitioning strategies
Creating and configuring mapping data flows
Using transformation activities like join, aggregate, and pivot
Debugging and testing data flows
Optimizing data flow performance
Creating and configuring schedule triggers
Implementing event-based triggers
Managing trigger dependencies
Monitoring and troubleshooting trigger executions
Configuring managed identities for secure access
Implementing data encryption at rest and in transit
Setting up role-based access control (RBAC)
Auditing and monitoring security logs
🏆
LEVEL 5

Expert

Analyzing business requirements for data integration
Designing data flow architecture
Selecting appropriate data sources and destinations
Implementing data ingestion strategies
Ensuring data quality and consistency
Documenting data integration processes
Setting up source control for Azure Data Factory
Creating ARM templates for data factory resources
Implementing CI/CD pipelines using Azure DevOps
Managing environment-specific configurations
Testing deployment processes
Rolling back deployments if necessary
Configuring diagnostic settings for Azure Data Factory
Setting up log analytics for pipeline monitoring
Creating custom alerts based on pipeline metrics
Integrating with Azure Monitor for comprehensive insights
Analyzing pipeline performance trends
Responding to and resolving alerts
Developing custom activities using Azure Functions
Integrating custom code with Azure Data Factory pipelines
Utilizing Azure Batch for large-scale data processing
Implementing custom connectors for non-native data sources
Testing and debugging custom activities
Documenting custom solutions for maintainability

Skill Overview

  • Expert2 years experience
  • Micro-skills96
  • Roles requiring skill0

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