Query advanced data sources, including JSON, Parquet, APIs, and Azure Machine Learning models

In the world of data analytics, it’s crucial to have the ability to access and query advanced data sources to gain valuable insights. Microsoft Azure and Power BI provide a comprehensive suite of tools and services that enable you to query various data sources such as JSON, Parquet, APIs, and Azure Machine Learning (ML) models. In this article, we will explore how you can leverage these data sources to design and implement enterprise-scale analytics solutions using Azure and Power BI.

1. Querying JSON Data Sources:

JSON (JavaScript Object Notation) is a popular format for storing and exchanging data. Azure and Power BI offer built-in support for querying JSON data sources, allowing you to extract meaningful information from structured and semi-structured data. To query JSON in Power BI, you can use the Power Query Editor, a powerful tool that enables you to import, transform, and analyze data. The editor provides an intuitive interface where you can apply filters, aggregations, and transformations to JSON data.

Here’s an example of how you can query JSON data in Power Query Editor:

let
    Source = Json.Document(File.Contents("C:\Data\sample.json")),
    Data = Source[data],
    Result = Table.FromRecords(Data)
in
    Result


In this code snippet, we load the JSON file from a local directory, extract the “data” field, and convert it into a tabular format.

2. Querying Parquet Data Sources:

Parquet is a columnar storage format that is optimized for big data processing. It offers advantages such as compressed storage, efficient columnar scans, and predicate pushdown optimizations. Power BI supports querying Parquet data sources, allowing you to leverage its benefits for fast and efficient analysis.

To query Parquet files in Power BI, you can use the Power Query Editor as well. Here’s an example:

let
    Source = Parquet.Document(File.Contents("C:\Data\sample.parquet")),
    Data = Source[data],
    Result = Table.FromRecords(Data)
in
    Result


In this script, we load the Parquet file, extract the “data” field, and convert it into a tabular format for further analysis.

3. Querying APIs:

Application Programming Interfaces (APIs) allow you to interact with external systems and retrieve data programmatically. Azure and Power BI provide capabilities to query RESTful APIs, enabling you to fetch real-time data from various sources.

To query an API in Power BI, you can use the “Web” connector in the Power Query Editor. Here’s an example:

let
    Source = Web.Contents("https://api.example.com/data"),
    Data = Json.Document(Source),
    Result = Table.FromRecords(Data)
in
    Result


In this code snippet, we specify the API URL, retrieve the data using the Web.Contents function, and convert the JSON response into a tabular format.

4. Querying Azure Machine Learning Models:

Azure Machine Learning empowers data scientists to build, deploy, and manage ML models at scale. Power BI integrates seamlessly with Azure ML, allowing you to query deployed ML models and consume their predictions in your reports and dashboards.

To query an Azure ML model in Power BI, you can use the “Invoke ML Web Service” connector in the Power Query Editor. Here’s an example:

let
    Source = AzureML.InvokeWebService("", "", [inputData])
in
    Source


In this code snippet, you invoke an ML web service by providing the endpoint URL and input data. The connector fetches the predictions from the ML model, which you can then utilize for further analysis and visualization.

Conclusion:

Querying advanced data sources such as JSON, Parquet, APIs, and Azure ML models is an essential aspect of designing and implementing enterprise-scale analytics solutions. With Azure and Power BI, you have the necessary tools and services at your disposal to extract valuable insights from diverse data sources. By leveraging the capabilities offered by Azure and Power BI, you can unlock the full potential of your data and drive informed business decisions.