Triangle SQL Server User Group

 
 
 

Concurrent inserts into a clustered table with an ever-increasing key become a bottleneck since Microsoft SQL Server must protect the corresponding pages with a page latch. There are several techniques like, hash partitioning, reserve bits, in-memory OLTP and the feature introduced in 2019 CTP 3.1 called OPTIMIZE_FOR_SEQUENTIAL_KEY. We will compare these features to understand their performance improvements and the effort required to modify your existing application.

Bio
Torsten (MVP, MCSE, MCT) has been working with Microsoft SQL Server since the turn of the millennium and is specialized in performance optimization for large SQL Server environments.

He has been with bp as a database engineer for over two decades and collaborates closely with Sarpedon Quality Lab as a Principal Microsoft SQL Server Consultant.

Torsten is the founder of inside-sqlserver.com and has given more than 170 presentations on Microsoft SQL Server performance optimization at national and international conferences and user groups.

Torsten has been recognized as a Microsoft Most Valuable Professional for Data Platform since 2018.

 
 
 

Query optimization is relatively easy when you look at a plan and find table scans, hash match joins, and missing indexes. But how do you find opportunities when all the low hanging fruit has been picked? This session is a case study on improving a procedure that runs 350 million times per day, so any improvement is greatly magnified. We will discuss where we found opportunities to improve a procedure running in 3.1 milliseconds; how effective our attempts were, what didn't work, and the results achieved.

Bio
Jared Poche began working with SQL Server as an instructor for certification classes and has a passion for teaching and performance troubleshooting. Jared spent 10 years providing customer support at Microsoft, most recently as a Sr. Support Escalation Engineer.

Jared is currently working as a database engineer for ChannelAdvisor, blogs about his recent experiences, and is developing online SQL Server courses.

 
 
 

Azure Synapse Analytics is a collection of data processing engines. In this session Armando Lacerda will demo how the Serverless SQL pool, which is part of Synapse Analytics, can be used with Power BI to implement data exploration on top of a data lake, making a simple implementation of the Lakehouse concept.

Bio
With over 35 years in the computing arena, Armando has been focused on data platform solutions around Microsoft Azure and helped many companies to migrate their systems to the cloud.

Armando has a passion for teaching and community engagement and has been certified as a Microsoft Trainer for over 25 years. He has been involved in presenting and helping other professionals better use and take advantage of Microsoft's data solutions.

Achieving his first Microsoft certification in 1996 as a Visual Basic 4.0 developer he was introduced to SQL Server 6.5 and 7.0 in 1998 and decided to focus his career on Microsoft's data platform. He has been certified in every release of the SQL product ecosystem for the past 20 years.