Microsoft Data Platform User Group Norway

Text/HTML

Welcome to Microsoft Data Platform User Group Norway!

PASSChapterLogo.jpgMicrosoft Data Platform User Group Norway (MDPUG Norway, formerly SQL Server User Group Norway) is for for everyone interested in or working with Microsoft Data Platform technologies including SQL Server, SQL Server Reporting Services & Analysis Services, Information Management (SQL Server Integration Services, Azure Data Factory (ADF) & Data Sync), Power BI, Analytics Platform System, Cortana Analytics Suite, Azure Data Lake, Azure DocumentDB, Azure HDInsight and Hadoop, Spark, & Storm on Azure, Azure Machine Learning, Azure Search, Azure SQL Data Warehouse, Azure SQL Database and Azure Stream Analytics. Our goals are to share knowledge, present new technologies, network and have fun. We welcome everyone from database administrators and developers to business intelligence professionals and students.

Membership is free, join us today! (Read more.)

We are always looking for speakers. Have you worked on an interesting project? Discovered a cool feature? Built an automated framework? Performance tuned the heck out of an application? Scripted all your daily tasks? Join us and share your experiences! We are looking for both Norwegian and English presentations of all levels.

Our meetings are managed via our Meetup group, you can find Microsoft Data Platform User Group Norway on LinkedIn and you can join us on Twitter by using the hashtag #MDPUGNorway. If you live in Oslo, follow Microsoft Data Platform User Group Oslo on Twitter (@MDPUG_Oslo) and use the hashtag #MDPUGOslo.

Next Meeting

Monday,

June

04

Adaptive Query Processing w/ M. Naumova & Azure SQL Performance w/ M. Jablonski

Yay!!!! MVP and MCM Magi Naumova is back to present and the topic this time around is Adaptive Query Processing in SQL Server 2017! This meetup will also feature Marek Jablonski on Azure SQL Performance.

Featured Presentation:

Adaptive Query Processing

Margarita Naumova, CEO SQL Master Academy

An improvement of cardinality estimation is something we try to find in every next version of SQL Server. Even with all the improvements the SQL Server 2014 Query Processor gained, we still need to deal with Hash and Sort Warnings, extremely high or low Memory grants, or the well-known User Defined Functions performance issues. That’s why we are so existed to find the new wave of adaptive query processing improvements to handle the more intractable cardinality estimation issues that often result in a poor query performance.

About Margarita:
Magi Naumova is SQL Server Architect and Consultant, speaker and trainer, Microsoft Certified Master, MVP Data Platform, MCT, Founder of SQL Master Academy, founder and leader of the Bulgarian SQL User group. She has more than 15 years SQL Server training and consulting experience. Magi is a former member of Microsoft Services Worldwide Technical Leadership Team, а Microsoft Women worth watching http://zd.net/1lwNYKH Currently she speaks, writes, trains and consults people on SQL Server technology. She runs a Data Platform consulting company part of which is SQL Master Academy training program. It helps hundreds of specialists to feel knowledgeable in their daily work or to find an inspirational career path in the world of SQL Server.

Azure SQL Performance

Marek Jablonski, CTO dbWatch AS

The popular image of cloud services is both attractive and enticing. You get access to an unlimited amount of hardware at a reasonable price. Your systems can run seamlessly and at peak performance anywhere in the world, and It is natural to assume that the performance of cloud services is identical and stable, regardless of whether it’s located in Asia, Europe or North America. To confirm this two tests periods were conducted. One in December 2016 and one in March 2018. Three Service tiers were tested: Basic 5 DTU, Standard S3 100 DTU and Premium P2 250 DTU. The tests have been done at three locations: West Europe, West Japan and West US. The test consisted of executing a locally defined Transact-SQL procedure that performed a specified number of insert, select, update and delete statements towards a locally defined table. Every test sequence was performed on 5K, 10K, 20K and 40K rows, and for Azure S3 and P2 the test runs were also performed on 80K and 160K rows. We present the method, results and findings.

About Marek:
Has Master of Science degree in astrophysics from the University of Oslo. Has worked with database technologies for over 20 years (first time DBA certified on Oracle 7.3, and MS SQL Server 6.5). Works as CTO in dbWatch (is the company's founder), which develops software for management of relational databases.

Partners and Sponsors:

Microsoft Webstep Innsikt Glasspaper dbWatch

 

Back to Top
cage-aids
cage-aids
cage-aids
cage-aids