The best thing about this book, is that it is free, there isn’t even a soul sucking registration to go through. Just click and download. It’s available is a standard 8.5×11″ PDF (standard US Pages) as well as a smaller PDF for mobile. If you are looking for EPUB and MOBI files you’ll need to wait a few more weeks as they are supposed to be available starting in January (don’t hold me to that, I’m just going off the MSDN post.
Denny has a chapter on SQL Server security improvements that looks particularly interesting to me.
For query workloads, we can see important information about every single query that hits the system including details such as the total duration of the query, query text (MDX/DAX), start and end times, as well as the associated user account. We can also determine details as to how the query was executed such as the number of partitions scanned, aggregation hits/misses, cache hits/misses, other queries running at the same time, etc…all of which have an effect on the performance of any one particular query. A secondary benefit is that we’ll be able to identify the usage pattern(s) of folks using the cube. For example, is usage low/moderate throughout the week with a heavy spike on Friday mornings?
Bonus note: it looks like there will be an xEvents for Analysis Services GUI in SQL Server 2016.
So I went through and converted everything in my Rtraining to this and realised it messed up my slide decks – it’s been so long since I had built a pure knitr solution that I forgot that
knitr::knit. For my slidedecks, if I wanted the ioslides_presentation format, I needed to use
rmarkdown::render. The problem with that has been the relative references to the CSS and the logo.
To solve this I read about the custom render formats capability and created afunction that produces an ioslides_presentation but with my CSS preloaded by default. This now means that I can produce slides with better file referencing.
Steph has put up all of her R-related presentations and documentation as well, so check that out.
DocumentDB organizes documents into collections, with each database capable of hosting one or more collection. Because DocumentDB is a cloud service, it offers quick and easy implementations, while delivering the flexibility and scalability necessary to meet the demands of todays web and mobile applications.
Read the whole thing if you’re interested in Microsoft’s competitor to MongoDB.
dbo.suspect_pagesis a table that resides in the MSDB database and is where SQL Server logs information about corrupt database pages (limited to 1,000 rows) that it encounters, not just when
DBCC CHECKBis run but during normal querying of the database. So if you have a DML operation that accesses a corrupt page, it will be logged here, this means that you have a chance of identifying a corruption in your database outside of the normal DBCC CHECKDB routine.
This is a nice tool we can use to check for corruption.
Detecting fraudulent transactions is a key applucation of statistical modeling, especially in an age of online transactions. R of course has many functions and packages suited to this purpose, including binary classification techniques such as logistic regression.
If you’d like to implement a fraud-detection application, the Cortana Analytics gallery features an Online Fraud Detection Template. This is a step-by step guide to building a web-service which will score transactions by likelihood of fraud, created in five steps
Read through for the five follow-up articles. This is a fantastic series and I plan to walk through it step by step myself.