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Ingesting JSON Data into Azure Data Explorer (Kusto) - A Comprehensive Guide

Azure Data Explorer (Kusto) is a fast, fully managed data analytics service for real-time analysis of large volumes of data streaming from applications, websites, and IoT devices. Ingesting data is the first step to leveraging its powerful query capabilities. This tutorial focuses on ingesting JSON formatted data into Azure Data Explorer, covering various methods with detailed explanations and code examples.

*Prerequisites:*

*Azure Subscription:* You'll need an active Azure subscription. If you don't have one, you can create a free trial account.
*Azure Data Explorer Cluster and Database:* Create an Azure Data Explorer cluster and a database within that cluster. You can do this through the Azure portal or using the Azure CLI.
*Kusto Query Language (KQL) familiarity:* Basic understanding of Kusto Query Language is helpful to query and manage the data.
*Appropriate Permissions:* Make sure your account has permissions to create tables, ingest data, and execute queries in the Azure Data Explorer database. The "Database Ingestor" role is commonly required.

*1. Understanding JSON Data and Kusto Schema:*

Before ingesting data, it's crucial to understand the structure of your JSON data and how it maps to the Kusto schema (table definition).

*JSON Structure:* JSON data is organized as key-value pairs within objects and arrays. Understand the types of data you'll be dealing with (strings, numbers, booleans, dates, nested objects, arrays).
*Kusto Schema:* A Kusto table is defined with a specific schema: a set of columns, each with a defined data type (e.g., `string`, `long`, `real`, `datetime`, `bool`, `dynamic`).
*Mapping:* Decide how each JSON field will map to a corresponding column in your Kusto table.

*Example JSON Data (sensor_data.json):*



*Example Kusto Table Definition (KQL):*



*Explanation:*

**`.create table SensorReadings`**: Crea ...

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