As an introduction to this series, I want to take you to the path that leads me to use Power Query here. You might be aware that I am teaching Power BI courses, and most of my courses are online and Live. This means that courses are not recorded videos, it is me on the other side of the line with full interactive audio and video experience with students with Go2Meeting application. Students connecting to me from other places in the world. So I do need an event date/time scheduler that I can announce date and time of the event in different time zones.
Fortunately there is a very good website that helps to find a date/time in different time zones. In this website I can set my input parameters as the date/time of my event locally (in my city), and name of the event, and duration.
Now that I have values in multiple columns I can concatenate them all into one string with Table.ToList function which converts a table to List. This function can concatenate all columns of table into one column (because List is a single columned data structure).
The actual concatenation happens by Combiner function; Combiner.CombineTextByDelimiter(“, “) which concatenate values with a delimiter which I set to be comma. So here is the expression for my new custom column:
Part 3 is forthcoming and should wrap up this series.
We first receive the order ID and the total amount of the order, and then we receive the line items of the order. The first value is the item ID, the second is the order ID, (which matches the order ID value) and then the cost of the item. In this example, we have two orders. The first one has four items and the second one has only one item.
The idea is to hide all of this from our Spark application, so what it receives on the DStream is a complete order defined on a stream as follows:
Check out this practical application of Spark Streaming.
Business users have been content to perform analytics on data collected in Amazon Redshift to spot trends. But recently, they have been asking AWS whether the latency can be reduced for real-time analysis. At the same time, they want to continue using the analytical tools they’re familiar with.
In this situation, we need a system that lets you capture the data stream in real time and use SQL to analyze it in real time.
In the earlier section, you learned how to build the pipeline to Amazon Redshift with Firehose and Lambda functions. The following illustration shows how to use Apache Spark Streaming on EMR to compute time window statistics from DynamoDB Streams. The computed data can be persisted to Amazon S3 and accessed with SparkSQL using Apache Zeppelin.
There are a lot of technologies at play here and it’s worth a perusal, even though I’m going to keep recommending that you use a relational database like SQL Server for OLTP work in all but the most extreme of circumstances.
This kind of attribution of an adjective to a subject is something an accurate parser can do automatically. Rather than laboriously going through just a hundred examples, it’s easy to set up the Parser McParseface and run through millions of sentences. The parser isn’t perfect, but at 94% accuracy on one metric, it’s pretty close to humans who get 96%.
Even better, having the computer do the heavy lifting means that it’s possible to explore many other relationships in the data, to uncover all sorts of unknown statistical relationships in the language we use. There’s bound to be other words that are skewed in similar or opposite ways to ‘bossy’, and I’d love to know what they are!
Looks like one more time sink for me… Check this out if you’re at all interested in parsers.
This appeared to work but didn’t display any results. When you look at the bottom status bar it reads
Comparison complete. No differences detected. Restricted comparison. See Error List for details.
I knew there we at least some differences. I then click on the Error List table below it which revealed
The reverse engineering operation cannot continue because you do not have View Definition permission on the ‘Warehouse’
The answer is pretty simple, so read on.
This is telling us that four of the tables in the sample do not have clustered indexes. Azure SQL Database insists on a clustered index for every table. So without warranty, here is a script that I used to refactor my on-prem AdventureWorksDW2014 database. After making these fixes, I was able to deploy to Azure SQL DB from SSMS with no errors.
Julie includes the script she used to fix up AdventureWorksDW.
We’ll aim to predict the volume of events for the next 10 minutes using a streaming regression model, and compare those results to a traditional batch prediction method. This prediction could then be used to dynamically scale compute resources, or for other business optimization. I will start out by describing how you would do the prediction through traditional batch processing methods using both Apache Impala (incubating) and Apache Spark, and then finish by showing how to more dynamically predict usage by using Spark Streaming.
Of course, the starting point for any prediction is a freshly updated data feed for the historic volume for which I want to forecast future volume. In this case, I discovered that Meetup.com has a very nice data feed that can be used for demonstration purposes. You can read more about the API here, but all you need to know at this point is that it provides a steady stream of RSVP volume that we can use to predict future RSVP volume.
This is pretty dense, but it is a great look at one potential architecture leveraging Spark and several tools in the Hadoop ecosystem.
Dashboards can be used to communicate a dense collection of information efficiently on a single canvas. Your audience has a limited amount of time to monitor key metrics to get a quick status and identify anything that needs attention. The attention span of the average human has gone from about 12 seconds in 2000 (when mobile phones became mainstream) to about 8 seconds today – a second less than a goldfish – according to a 2015 study.
Following data visualization design principles is key to making your dashboard easily consumable. A poorly designed dashboard can make your eyes jump all over the screen. While it won’t give you much insight, it may cause a headache. In the Western world, we read from top left to right, then zig-zag down left and scroll right again (in a Z-pattern). Understanding where the audience’s eyes will start and travel next allows you to guide them through your dashboard.
Check the link for more details.
Sander Stad has a couple of blog posts on working with Windows Server Core edition. First, what happens if you lose your command prompt?
In my enthusiasm I clicked the “X” on the top right corner. So this happened:
Mayhem! How do I get my command screen back? Reboot?! NO WAY!
After figuring that out, Sander also explains how to perform updates:
The Windows Server Core Edition is a really good option because there aren’t that many binaries as there would be in a full installation. Due to the smaller number of objects you more stability, simplified management, reduced maintenance and a reduced risk to get attacked.
So you have a Windows Server Core Edition installed but want to update the server manually. Maybe this is a virtual machine on your local PC that needs updating and you don’t have WSUS running.
About everything you previously managed using the GUI in Windows is now done with the sconfig. You can edit your server’s name, the domain, network settings, date/time, shutdown the server but also manage the updates.
Admittedly, most Core installations will probably be in environments with a lot of automation around them, but sometimes you’re just doing a one-off thing.
It has to be said, if performance is important (and it should be), you’ll know that user-defined scalar T-SQL functions can kill your query. So you might want to implement the above in the form of a CROSS APPLY instead
Here’s where I advocate for two tables on every instance: a tally table and a date table. The date table should look like a date dimension, including every potentially-interesting piece of information about a date—including if it’s a weekday. It might not perform quite as fast as Daniel’s solution (I’d have to test to know for sure), but it’s definitely easier. If you can’t get a viable date table on your instances for whatever reason, Daniel’s solution does work and does not require any additional objects.