Silvano Coriani, Author at Azure SQL Devs’ Corner

Silvano Coriani

Principal Program Manager, Azure SQL Database

Silvano Coriani is a Principal Program Manager in Azure SQL Database team. He has 20+ years' experience in application development and database design, troubleshooting and performance tuning. As self-employed first, and then part of SQL CAT and Azure Data engineering teams, he presented at several industry leading conferences and co-wrote several articles on data related topics.

Post by this author

Go Azure SQL!

June 16th, 2020

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!

June 10th, 2020

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

June 4th, 2020

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

May 28th, 2020

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.