One of the sessions I was most looking forward to at Microsoft Ignite 2017 was New capabilities for data integration in the cloud with Mike Flasko. In that session, he talks about Azure Data Factory (ADF) v2 and its new first-class SSIS support.
After the session, I convinced Mike Flasko and Sanjay Krishnamurthi to have a chat with me :) We talked about what’s new in Azure Data Factory v2, including the updated pipeline application model with a new visual design canvas, new Software Development Kits (SDKs) for working with Azure Data Factory, the new Integration Runtime, and the ability to run SSIS packages inside Azure Data Factory v2.
Azure Data Factory v2 with Mike Flasko
Follow Mike Flasko on Twitter @mflasko, and keep an eye out for more news about ADF and SSIS! I may or may not have convinced him to do another interview with me in a couple of months :)
At Microsoft Ignite 2017, I had planned an interview with Sunil Agarwal, and was very excited about it. Then Sunil asked if he could bring Kevin Farlee. Of course! Then he asked if he could also bring their customer, Aaron Gerdeman from FIS. Even better! :)
In this interview, we chat about SQL Server 2017, Resumable Index Builds, Adaptive Query Processing, Columnstore Indexes, High Availability, Real-time Analytics, Real-time Dashboards and the SQL Tiger Team.
High Performance Analytics with Sunil Agarwal, Kevin Farlee, Aaron Gerdeman
I know Bob had a very busy schedule at Microsoft Ignite, so I’m very thankful he was able to spend a few minutes with me! I hope you find this as interesting as I did :) If you want to learn even more, you can watch his session Experience Microsoft SQL Server 2017: The fast and the furious from Microsoft Ignite 2017:
I got to interview Buck Woody about Data Science at Microsoft Ignite 2017! :)
In this interview, we chat about Microsoft Business Analytics and AI (formerly known as Cortana Intelligence Suite), Artificial Intelligence in Excel, intent-based programming, Predictive Analytics, DevOps for Data Scientists and life-long learning.
Data Science with Buck Woody – Microsoft Ignite 2017
I’m so thankful that Buck took some time out of his busy schedule to chat with me, and I hope all of you enjoy this interview. If you want to learn more, watch his full session DevOps for data science from Microsoft Ignite 2017:
Inserts, updates and deletes on large tables can be very slow and expensive, cause locking and blocking, and even fill up the transaction log. One of the main benefits of table partitioning is that you can speed up loading and archiving of data by using partition switching.
Partition switching moves entire partitions between tables almost instantly. It is extremely fast because it is a metadata-only operation that updates the location of the data, no data is physically moved. New data can be loaded to separate tables and then switched in, old data can be switched out to separate tables and then archived or purged. All data preparation and manipulation can be done in separate tables without affecting the partitioned table.