Azure SQL Devs’ Corner

Developing in the cloud with SQL Server Big Data Clusters: Getting Started

Melony Qin August 6, 2020 Aug 6, 2020 08/6/20

When it comes to innovation, we realized that the world’s most valuable resource is no longer oil, but data. One of the biggest challenges we’re having today is how to integrate disparate data sources from many different places. If you're looking into how to work on Big Data Analytics solutions in Kubernetes, that's where Big Data Clusters...

Architecting REST API with Python, Flask and Azure SQL

Davide Mauri July 22, 2020 Jul 22, 2020 07/22/20

Creating REST API with Azure SQL is never been easier thanks to its native JSON support. Let's see how we can use Python and Flask to create an elegant and modern solution to serve REST API to the world

Preview of Azure Arc enabled SQL Server is now available

Alexander (Sasha) Nosov July 2, 2020 Jul 2, 2020 07/2/20

The preview allows you to track the inventory of your SQL Server instances across on-premises, edge sites, and multi-cloud in a single view. You can also use Azure Security Center and Azure Sentinel to protect your SQL server using advanced data security features.

Querying and visualizing data using SQLPad

Davide Mauri June 30, 2020 Jun 30, 2020 06/30/20

SQLPad is an amazing free, open source, tool to run SQL Queries against a broad spectrum of popular databases, without the need to install and run something on-premises. It's lightweight, simple and just perfect if you need a no-frills tool to query and visualize data, to do some data exploration.

Use Linked server to Synapse SQL to implement Polybase-like scenarios in Managed Instance

Jovan Popovic June 24, 2020 Jun 24, 2020 06/24/20

Serverless Synapse SQL pool enables you to query files on Azure Data Lake Storage. Azure SQL Managed Instance can use linked servers to query data on Azure Storage using Synapse Analytics serverless SQL query endpoint. This technique linked server may be good solution for some Polybase-like scenarios.

Microsoft.Data.SqlClient 2.0.0 is now available

Davide Mauri June 19, 2020 Jun 19, 2020 06/19/20

Microsoft.Data.SqlClient version 2.0.0 has been released, with several interesting features. Make sure you check it out if you are a .NET developer and you are using Azure SQL or SQL Server in your solutions. Microsoft.Data.SqlClient is the new, open source, official data access library that replaces System.Data.SqlClient

Go Azure SQL!

Silvano Coriani June 16, 2020 Jun 16, 2020 06/16/20

Go is a very popular programming language for developing microservices, Web APIs and other server-side applications, and Azure SQL can definitely be an option where to persist data for these applications in a scalable, reliable and modern way leveraging Microsoft SQL Server Driver for Go and ORM packages like Gorm. Give it a try!

JSON in your Azure SQL Database? Let’s benchmark some options!

Silvano Coriani June 10, 2020 Jun 10, 2020 06/10/20

Introduction Storing and retrieving data from JSON fragments is a common need in many application scenarios, like IoT solutions or microservice-based architectures. You can persist these fragments can be in a variety of data stores, from blob or file shares, to relational and non-relational databases, and there’s a long standing debate in...

Improve JDBC application reliability with Azure SQL and connection pooling

Silvano Coriani June 4, 2020 Jun 4, 2020 06/4/20

A growing number of Azure SQL Database customers are developing new applications in Java using a JDBC driver from Microsoft or a 3rd party. These drivers are providing quite extensive support and covering most of database service capabilities and performance expectations but there are a couple of areas that deserve some attention.

Optimize Azure SQL Upsert scenarios

Silvano Coriani May 28, 2020 May 28, 2020 05/28/20

Customers often need to move a dataset from a source system into a new destination, inserting rows that doesn't exist in a target table and update those that already exists. With this technique, we've been able to reduce the time needed to upsert a dataset of 2M rows against a target table with 30M rows from 20 hours to 20 minutes.