Describe common formats for data files

Data files play a crucial role in the field of data analytics and Azure data services. Microsoft Azure offers various formats for storing and processing data files, allowing users to choose the most suitable option based on their specific requirements. In this article, we will explore the common formats for data files related to the Microsoft Azure Data Fundamentals exam.

1. CSV (Comma-Separated Values):

CSV is a simple and widely used format for storing structured data files. In CSV format, each line represents a row, and the values within the row are separated by commas. Azure services such as Azure Data Factory, Azure Databricks, and Azure Machine Learning support CSV files. Here’s an example of a CSV file:

Name, Age, City
John Doe, 25, New York
Jane Smith, 30, London

2. JSON (JavaScript Object Notation):

JSON is a lightweight and human-readable format for representing structured data. It is commonly used for data transfer and storage. JSON files in Azure often contain arrays and nested objects. Azure services like Azure Cosmos DB, Azure Functions, and Azure Stream Analytics support JSON files. Here’s an example of a JSON file:

[
{
"Name": "John Doe",
"Age": 25,
"City": "New York"
},
{
"Name": "Jane Smith",
"Age": 30,
"City": "London"
}
]

3. Parquet:

Parquet is a columnar storage format that provides efficient compression and encoding schemes, making it ideal for big data processing. It offers fast data retrieval, low storage costs, and high performance. Azure services like Azure Synapse Analytics and Azure Databricks support Parquet files. Here’s an example of Parquet file structure:

- file.parquet
- _metadata
- part-00000.snappy.parquet
- part-00001.snappy.parquet
- ...

4. Avro:

Avro is a binary serialization format that enables efficient data exchange between applications and provides schema evolution support. It offers rich data structures with a compact size, making it suitable for high-performance processing. Azure services such as Azure HDInsight and Azure Databricks support Avro files. Here’s an example of Avro file structure:

- file.avro
- ...

5. ORC (Optimized Row Columnar):

ORC is a self-describing columnar file format that provides efficient data compression and high data processing performance. It is widely used in big data analytics workloads. Azure services like Azure Data Lake Storage and Azure Databricks support ORC files. Here’s an example of ORC file structure:

- file.orc
- ...

6. Apache Parquet with Snappy Compression:

Apache Parquet with Snappy compression is a combination of the Parquet file format and the Snappy compression algorithm. Snappy compression provides fast and efficient data compression, enabling high-performance processing. Azure services like Azure Synapse Analytics support Parquet files with Snappy compression. Here’s an example of a Parquet file with Snappy compression structure:

- file.snappy.parquet
- ...

These are some of the common file formats used in Microsoft Azure for storing and processing data. Each format has its own advantages and is suitable for specific scenarios. By understanding these formats, you can effectively work with data files in Azure and optimize your data processing workflows.