Azure data factory Data Types

Azure Data Factory (ADF) supports a variety of data types to accommodate diverse sources and destinations in data workflows. The supported data types include:

String (varchar, char): Used for representing character data.

Boolean (bit): Represents true/false or binary values.

Numeric (int, float, decimal): Used for numeric data, with options for integers, floating-point numbers, and decimals.

Date and Time (datetime, timestamp): Represents date and time values.

Binary (Var binary): Stores binary data, such as images or documents.

JSON: Used for handling JSON (JavaScript Object Notation) data.

XML: For handling Extensible Markup Language data.

Geospatial: Supports spatial data types for handling geographical information.

Array/Collection: Represents a collection of values.

Table: A structured format for organizing data with rows and columns.

1. Azure Data Lake Storage Data Types:

• Avro: A compact binary format suitable for serialization and data exchange.
• ORC (Optimized Row Columnar): A columnar storage file format that provides high compression and performance.
• Parquet: A columnar storage file format optimized for use with big data processing frameworks.

2. Azure SQL Data Warehouse (SQL DW) Data Types:

• ADF supports the data types used in Azure SQL Data Warehouse, including variations of numeric, string, date, and time types.

3. File Formats:

• ADF supports various file formats like CSV, TSV, JSON, and others, allowing flexibility in handling different types of structured and semi-structured data.

4. Structured, Semi-structured, and Unstructured Data:

• ADF is designed to work with structured data (tabular data with a fixed schema), semi-structured data (like JSON and XML), and unstructured data (binary data or documents).

5. Data Type Mapping:

• ADF provides data type mapping and transformation capabilities during data movement and transformation activities, ensuring compatibility between source and destination data types.

6. Custom Data Types:

• ADF allows users to define custom data types, providing flexibility to accommodate specific data structures or requirements unique to their use case.

7. Support for Azure Synapse Analytics:

• ADF integrates with Azure Synapse Analytics, supporting its specific data types for a seamless end-to-end data integration process.