Managed instance is a new deployment option of Azure SQL Database, providing near 100% compatibility with the latest SQL Server on-premises (Enterprise Edition) Database Engine, providing a native virtual network (VNet) implementation that addresses common security concerns, and a business model favourable for on-premises SQL Server customers. Implemented and led all surface area efforts including UI (on Azure Portal), REST APIs and client tools (Powershell, Commandlets etc) on the project.

Azure SQL Database Managed Instance is for SQL Server implementation on Azure cloud to keep all database files on Azure storage. Created the virtual storage size limit and file size to account for work on both remote and local storage and simulate the performance of disks of certain sizes. Built the full change performance functionality into the solution; allowing customers to change the number of virtual cores and amount of storage. Provided full stack expertise on the project by implementing changes in the SQL Engine (C++ codebase), SQL Azure management workflows and APIs and SQL Azure Portal.

Azure SQL Database Automatic tuning provides peak performance and stable workloads through continuous performance tuning based on AI and machine learning. Automatic tuning is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executed on a database, and it automatically improves its performance. Led a team to implement full surface area solutions for configuring automatic tuning (UI, APIs, PowerShell, T-SQL) for both SQL Azure and SQL on-prem and pushed automatic tuning as on-by-default. Actively participated in development on all layers of the stack on the project.

Azure SQL Database identifies potential problems in your database and recommends actions that can improve the performance of your workload by providing intelligent tuning actions and recommendations. Worked with a team of two developers to implement a unified UI for monitoring SQL DB performance and applying performance tuning recommendations on the database.

Azure SQL Database learns and adapts with your application. It provides customized recommendations that enable users to maximize the performance of their SQL databases. SQL Database continuously assesses and analyzes the usage history of SQL databases. The solution exposes all performance tuning recommendations created by the data scientist through an API and Azure Portal. Worked as a frontend developer, provided fullstack expertise and on-boarded a few engineers onto the project. Led the whole full-stack effort, bridging the gap between program managers, designers and data scientist to ensure that all parts of the stacks on the project are in sync.

Query Performance Insight is a part of the Azure SQL Database intelligent performance product line. It helps users spend less time troubleshooting database performance by providing them with deeper insight into their databases resource (DTU) consumption, details on top of database queries by CPU, duration, and execution count (potential tuning candidates for performance improvements). It gives users the ability to drill down into details of a query, to view the query text and history of resource utilization and annotations that show performance recommendations from SQL Database Advisor. Built the UI for visualization of resource utilization of SQL DB with per query breakdown on the project.

SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single Azure SQL Database server and share a set number of resources at a set price. Elastic pools in Azure SQL Database enable SaaS developers to optimize the price performance for a group of databases within a prescribed budget while delivering performance elasticity for each database. Implemented key parts of the UI for configuring Elastic Pool pricing tier.