Microsoft Azure Data Factory

1. Data Integration: Ability to ingest data from diverse sources such as databases, files, applications, and cloud services, and load it into a centralized data store or data lake.

2. ETL Orchestration: Capability to define and orchestrate complex data workflows and transformations using a visual interface or code-based scripting languages like Python and SQL.

3. Scalability and Performance: Scalable architecture designed to handle large volumes of data and process workloads efficiently, with built-in parallel execution and distributed computing capabilities.

4. Data Transformation: Support for data cleansing, enrichment, and transformation tasks using built-in data transformation activities and integration with external data processing services like Azure Databricks and HDInsight.

Before learning Microsoft Azure Data Factory, it's beneficial to have the following skills:

1. Understanding of Data Integration Concepts: Familiarity with data integration concepts such as ETL (Extract, Transform, Load), data pipelines, data lakes, and data warehouses.

2. Knowledge of Data Formats and Protocols: Understanding of common data formats (e.g., JSON, XML, CSV) and data transfer protocols (e.g., HTTP, FTP, REST) used in data integration.

3. SQL and Data Manipulation Skills: Proficiency in SQL (Structured Query Language) for querying and manipulating data in relational databases, as Azure Data Factory supports SQL-based transformations.

4. Familiarity with Azure Services: Basic knowledge of Microsoft Azure services and their functionalities, including Azure Storage, Azure SQL Database, Azure Data Lake Storage, and Azure Synapse Analytics.

By learning Microsoft Azure Data Factory, you gain the following skills:

1. Data Integration: Ability to design, build, and manage data integration pipelines to ingest, transform, and load data from diverse sources into Azure data services.

2. ETL Orchestration: Skills to orchestrate complex data workflows and transformations using a visual interface or code-based scripting, enabling efficient data movement and processing.

3. Data Transformation: Proficiency in performing data transformations, cleansing, and enrichment tasks to prepare data for analytics, reporting, and other data-driven applications.

4. Azure Services Integration: Knowledge of integrating Azure Data Factory with other Azure services such as Azure Synapse Analytics, Azure Databricks, and Azure SQL Database for comprehensive data processing and analysis.