Recommend appropriate file types for querying serverless SQL pools

When working with serverless SQL pools in Azure, it is important to choose the appropriate file types for querying and optimizing your analytics solutions. In this article, we will explore different file types and their use cases in the context of designing and implementing enterprise-scale analytics solutions using Microsoft Azure and Microsoft Power BI.

1. CSV (Comma-Separated Values)

CSV files are a popular choice for storing tabular data as plain text. They are widely supported and can be easily imported and exported across various applications and platforms. When working with serverless SQL pools, CSV files can be queried efficiently using PolyBase, a feature that enables seamless integration between SQL Server and Azure SQL Data Warehouse. Here’s an example of how to create an external table for querying a CSV file:

CREATE EXTERNAL TABLE [dbo].[MyCsvData]
(
[Column1] INT,
[Column2] VARCHAR(50),
[Column3] DECIMAL(18, 2)
)
WITH
(
LOCATION = ‘https://myaccount.blob.core.windows.net/mycontainer/mydata.csv’,
DATA_SOURCE = MyAzureStorageAccount,
FILE_FORMAT = CsvFileFormat
);

2. Parquet

Parquet is a columnar storage file format that is highly efficient for analytics workloads. It provides excellent compression and performance characteristics by storing data in a column-wise manner. Parquet files are suitable for large datasets and can be integrated with Azure Data Lake Storage or Azure Blob Storage. To query Parquet files stored in Azure Blob Storage, you can create an external table using the following code snippet:

CREATE EXTERNAL TABLE [dbo].[MyParquetData]
(
[Column1] INT,
[Column2] VARCHAR(50),
[Column3] DECIMAL(18, 2)
)
WITH
(
LOCATION = ‘https://myaccount.blob.core.windows.net/mycontainer/mydata.parquet’,
DATA_SOURCE = MyAzureStorageAccount,
FILE_FORMAT = ParquetFileFormat
);

3. Avro

Avro is a binary serialization format that provides a compact and efficient way to store data, including the schema, in a self-describing format. It supports rich data structures and is well-suited for data exchange between different systems. You can query Avro files in serverless SQL pools by creating an external table using the Avro file format:

CREATE EXTERNAL TABLE [dbo].[MyAvroData]
(
[Column1] INT,
[Column2] VARCHAR(50),
[Column3] DECIMAL(18, 2)
)
WITH
(
LOCATION = ‘https://myaccount.blob.core.windows.net/mycontainer/mydata.avro’,
DATA_SOURCE = MyAzureStorageAccount,
FILE_FORMAT = AvroFileFormat
);

4. ORC (Optimized Row Columnar)

ORC is another columnar storage file format that offers efficient compression and improved query performance. ORC files are particularly suitable for complex analytics queries involving large datasets. You can integrate ORC files with Azure Data Lake Storage or Azure Blob Storage to query data in serverless SQL pools:

CREATE EXTERNAL TABLE [dbo].[MyOrcData]
(
[Column1] INT,
[Column2] VARCHAR(50),
[Column3] DECIMAL(18, 2)
)
WITH
(
LOCATION = ‘https://myaccount.blob.core.windows.net/mycontainer/mydata.orc’,
DATA_SOURCE = MyAzureStorageAccount,
FILE_FORMAT = OrcFileFormat
);

5. JSON (JavaScript Object Notation)

JSON is a lightweight and widely-used file format for storing and exchanging structured data. It is human-readable and supports complex data structures. JSON files can be queried in serverless SQL pools using PolyBase. Here is an example of creating an external table for querying a JSON file:

CREATE EXTERNAL TABLE [dbo].[MyJsonData]
(
[Column1] INT,
[Column2] VARCHAR(50),
[Column3] DECIMAL(18, 2)
)
WITH
(
LOCATION = ‘https://myaccount.blob.core.windows.net/mycontainer/mydata.json’,
DATA_SOURCE = MyAzureStorageAccount,
FILE_FORMAT = JsonFileFormat
);

In conclusion, when designing and implementing enterprise-scale analytics solutions in Azure, it is important to choose the appropriate file type for querying serverless SQL pools. CSV, Parquet, Avro, ORC, and JSON are all popular file formats supported by Azure services. By leveraging the right file type, you can optimize data storage, query performance, and integration with other Azure services like Azure Data Lake Storage and Azure Blob Storage.