On Being an "Expert"

I had the opportunity last week to present a session at DataSaturdays Stockholm. I can't say enough nice things about the event and the organizers. I presented on Query Store in SQL Server. In my mind, it's an entry-level to middle tier presentation. Most of the room was already working with Query Store, some quite […]

Adding Hints with Query Store

Enhancing Web Application Performance through Database Optimization: A Comprehensive Study

In the contemporary digital landscape, web applications play a pivotal role in delivering services and facilitating interactions between users and businesses. However, the performance of these applications heavily relies on the efficiency of underlying databases. This comprehensive study aims to explore the intricate relationship between database optimization techniques and web application performance enhancement. The study begins by delineating the significance of web application performance and its correlation with database operations. It examines various factors affecting performance, including data retrieval, storage, and processing. Furthermore, it identifies common challenges such as latency, scalability issues, and inefficient query execution. Through an extensive review of the literature, this study evaluates state-of-the-art database optimization methodologies and their applicability to web applications. Techniques such as indexing, query optimization, denormalization, caching mechanisms, and data partitioning are scrutinized, highlighting their potential to mitigate performance bottlenecks. Moreover, the study investigates emerging trends and technologies in database management systems (DBMS), such as NoSQL databases, in-memory databases, and cloud-based solutions. It assesses their suitability for enhancing web application performance, considering factors like data volume, query complexity, and real-time processing requirements.

Multiple Query Trace Flags

How Big Data Supports Gen AI

This article includes an overview of how big data and Artificial Intelligence (AI) models work together.

Combining Silver Tables into a Model – Data Engineering with Fabric

This next article in the series creates objects at the gold layer for consumption by combining tables from the silver layer of the lake house.

Using Machine Learning Pipeline Execution Activity in Azure Data Factory

This article shows how one can run a machine learning activity in an Azure Data Factory (ADF) pipeline.

Take Care

The world seems to be proceeding through some stuff at the moment. IT and our industry as a whole, equally seems to be going through some stuff. As such, allow this oldster to offer some advice: Take care of yourself. I know, I'm the same, you may have responsibilities for others. You need to take […]

One more reason to use foreign key constraints

Since the title might be considered a bit vague, I don’t want you to wade through the article to figure it out. I will spare you the typical clickbait introduction, with me telling you what a foreign key constraint is, and why it and all the other constraint types provided by relational engines are useful. […]

Using Table Hints in a Query