This article will help you understand how to analyze Azure Cosmos DB data using Azure Synapse Analytics.

In this article, we will explore joining database tables using U-SQL scripts for Azure Data Lake Analytics.

This article will walk you through how you can deploy a sample model on Azure Analysis Services and you will also learn the way to consume it using different tools.

In this article, we will learn to create external tables in Azure Synapse Analytics with dedicated SQL pools.

In this article, we will learn how to create a Spark pool in Azure Synapse Analytics and process the data using it.

This article helps you create a dedicated SQL pool in Azure Synapse Analytics, which is the first step to set up a data warehousing environment in Synapse.

This article will show how to work with SQL on-demand pools and understand the fundamentals of working with this pool in Azure Synapse Analytics.

In this article, you learn how to set up Azure Data Sync services. In addition, you will also learn how to create and set up a data sync group between Azure SQL database and on-premises SQL Server.

This article will help you get started with Azure Synapse Studio and its various features.

This article will help you understand the process to create an Azure Synapse Analytics workspace and some other features related to it.

In this article, we will see how we can access data from an Azure SQL database from Azure Data Lake Analytics.

This article will describe how to get started with Azure Analysis Services and help you understand the configuration and pricing options to create our first Analysis Services instance.

This article will help you process file sets with U-SQL in Azure Data Lake Analytics.

In the previous articles of this Azure Data Factory series, we showed different scenarios in which we can take advantage of the Azure Data Factory in copying data from variant data stores and optionally transform the data before loading it to the destination datastore. In all examples that are provided in the previous articles, we created a new pipeline from scratch to achieve the required functionality.

In the previous articles of this series, we showed how to create Azure Data Factory pipelines that consist of multiple activities to perform different actions, where the activities will be executed sequentially. This means that the next activity will not be executed until the previous activity is executed successfully without any issue.

This article will help you create database objects in Azure Data Lake Analytics using U-SQL.

In the previous article, How to schedule Azure Data Factory pipeline executions using Triggers, we discussed the three main types of the Azure Data Factory triggers, how to configure it then use it to schedule a pipeline.

In this article, I am going to explain how to create a serverless application using Azure Functions and use Azure SQL Database to log messages generated by the function. In this world of cloud-based applications, it is very important that you are aware of how to create and design serverless applications. An important aspect while designing any application is to generate log messages at every key step or operation that is being performed. This helps us to understand the workflow whenever there are some issues and need debugging at some later point in time.