Thank you to everyone who attended my webinar last week! In that session, I went through all the steps to install SQL Server to be able to use Hadoop, but I thought it might also be a good idea to include them here so you don’t have to watch the video.
There may be a time when you will need to create a table containing a series of dates. Perhaps you want a date table for a data warehouse or a data model in Excel’s Power Pivot. There are a number of ways you can create a date table in SQL Server. I will show a method, which we recommend at Pragmatic Works, that uses a Common Table Expression [CTE].
Datazen is a reporting tool recently acquired by Microsoft that allows dashboard creation and publishing based on many data sources, including Excel. With Microsoft's acquisition comes many questions about the Datazen interface. In this post, Ginger Grant will give you insight on creating Datazen dashboards.
Recently I was afforded the opportunity to speak at several different events, all of which I thoroughly enjoyed. I was able to speak on Azure Machine Learning first at the Arizona SQL Server Users Group meeting. I really appreciate all who attended as we had quite a crowd. Since the meeting is held practically on Arizona State University’s Tempe Campus, it was great to see a number of students attending, most likely due to Ram’s continued marketing efforts on meetup.com. After talking to him about it, I was impressed at his success at improving attendance by promoting the event on Meetup, and wonder if many SQL Server User Groups have experienced the same benefits. If you have, please let me know. Thanks Joe for taking a picture of the event too.
As I was honored enough to be selected to give a PreCon on the Internals of the Modern Data Warehouse at SQLSaturday Huntington Beach, I thought that I would take the time to explain why I felt drawn to the topic. There are a lot of places that haven’t given much thought to the changes in technology which have happened over the last few years. The major feature upgrades to SQL Server in 2012 and 2014 have meant that they can use column store indexes which makes things faster and maybe better High Availability. While those things are certainly valuable improvements there is a lot more that you can do to derive value from your data and companies want more than just a well-organized, running data warehouse.
Business and predictive analytics are the pinnacle of a mature data lifecycle, and Machine Learning is an exceptional tool providing in-depth statistical analysis of data. One of the common misconceptions about Machine Learning is the level of mathematical experience required, and Pragmatic Works' Business Intelligence Consultant and Data Aficionado Ginger Grant is on a mission to set the record straight. In her upcoming webinar, Complex Data Analysis and Azure Machine Learning, she'll be explaining how to leverage Azure ML to analyze cloud data without having to know a lot of complex formulas.
It can be very difficult to keep up with the increasing number of changes in technology. There are so many things changing with the move to the cloud, data storage and access, self-service visualization tools and not to mention the many enhancements Microsoft has made in the latest versions of SQL Server. One place where it is possible to drink from the fire hose, so to speak, is at SQLSaturday events. Being able to find things out from the people who literally wrote the book on SQL Server, well technically wrote a bunch of books, is a great opportunity to catch up on a lot of new technology.
Thank you to all of the people who took the time to view my session on Data Analytics and Distribution with Power BI. I really enjoy getting the chance to decrease the confusion I hear regarding Power BI, and hope that you will find the question and answer section helpful if you are trying to learn more about the product.