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Author: Kevin Feasel

Hurricane Matthew Tracking With Power BI

Published 2016-10-07 by Kevin Feasel

Chris Albrektson has a Power BI report tracking Hurricane Matthew:

We’ve got company and it’s not the type of company that you want! As most Floridians are preparing for Hurricane Matthew I thought it might be neat to track the storm using PowerBi. So I went out and found some public data online and brought that into PowerBi, created a couple calculations and some visualizations.

My goal for this was to create a report where I could track the storm no matter where I was. I also needed the ability to see the latest data without any manual intervention. PowerBi can handle all of this for me utilizing the PowerBi Mobile app and a few other cool features.

Good use of Power BI here.

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Use Folders With Polybase

Published 2016-10-06 by Kevin Feasel

Andrew Peterson argues that you should use folders instead of individual files when creating external tables:

Why?
1) Add more files to the directory, and Polybase External table will automagically read them.
2) Do INSERTS and UPDATES from PolyBase back to your files in Hadoop.
( See PolyBase – Insert data into a Hadoop Hue Directory ,
PolyBase – Insert data into new Hadoop Directory    ).
3) It’s cleaner.

This is good advice.  Also, if you’re using some other process to load data—for example, a map-reduce job or Spark job—you might have many smaller file chunks based on what the reducers spit out.  It’s not a bad idea to cat those file chunks together, but at least if you use a folder for your external data location, your downstream processes will still work as expected regardless of how the data is laid out.

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NetworkD3

Published 2016-10-06 by Kevin Feasel

Vessy combines Javascript and R to visualize networks:

The networkD3 package provides a function called igraph_to_networkD3, that uses an igraph object to convert it into a format that networkD3 uses to create a network representation. As I used igraph object to store my network, including node and edge properties, I was hoping that I may only need to use this function to create a visualization of my network. However, this function does not work exactly like that (which is not that surprising, given the differences in how D3.js works and how igraph object is defined). Instead, it extracts lists of nodes and edges from the igraph object, but not the information about all node and edges properties (the exception is a priori specified information about nodes membership groups/clusters, which can be derived from one or more network properties, e.g., node degree). Additionally, the igraph_to_networkD3 function does not plot the network itself, but only extracts parameters that are later used in theforceNetwork function that plots the network.

This is the kind of thing I want to see when working with network data.  It doesn’t necessarily scale, but given how well the human eye tracks relationships, this is very useful.

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Understanding Page Splits

Published 2016-10-06 by Kevin Feasel

Wayne Sheffield goes into detail on page splits:

In considering which of these methods is preferred, we need to consider whether page splits impact these methods – especially nasty page splits. Furthermore, how will index maintenance affect each choice? So let’s think this through.

When there are negative values in this column, and the index is rebuilt, there will be a page with both negative and positive values in it. If the identity column is set to (-1, -1), there won’t be a gap (excluding the 0) in the values, and newly added rows will get a new page allocated – a good page split. If the identity column is set to (-2147483648 , 1), then there will be a full page with the records for the most recently used identity value, and with the values starting with 1 – a rather large gap.

This is worth reading in its entirety.

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Log Buffers

Published 2016-10-06 by Kevin Feasel

Mark Broadbent has started a series on transaction durability.  His first topic is the log buffer:

SQL Server is a highly efficient transaction processing platform and nearly every single operation performed by it, is usually first performed within memory. When operations are performed within memory, the need to touch physical resources (such as physical disk IOPS) are also reduced, and reducing the need to touch physical resources means those physical boundaries (and their limitations) have less impact to the overall system performance. Cool right?!

Click through to read more about how log buffers work and why they help improve SQL Server’s performance.

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Navigating Complex Tabular Models

Published 2016-10-06 by Kevin Feasel

Bill Anton has a method for understanding large tabular models on legacy platforms:

Unfortunately, the Tabular Model Explorer is only available for 2016 (compatibility 1200) tabular models – which many folks haven’t moved over to just yet (despite the overwhelming list of reasons why SQL 2016 is one of the best releases in a very long time).

Those of us stuck with 2012/2014 environments have no other option than to comb through the diagram view for that one table we’re looking for…or scan the unordered list of tables/columns in grid view, or arrow-key through a bunch of cells in the calculation pain pane to find a particular measure… or so I thought up until a few weeks ago when I discovered a better way!

At least 60-70% of the DBA population would chafe at the idea that is a “legacy” platform, so maybe I’m overstepping it a little with calling 2014 legacy.  But seriously, 2016 is a huge improvement, well worth the jump.

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Sentiment Analysis With Nifi

Published 2016-10-06 by Kevin Feasel

Timothy Spann ties together a bunch of interesting things with Apache Nifi, including integrations with Twitter, Slack, Tensorflow, and Zeppelin:

First up, I used GetTwitter to read tweets and filtered on these terms:

strata, stratahadoop, strataconf, NIFI, FutureOfData, ApacheNiFi, Hortonworks, Hadoop, ApacheHive, HBase, ApacheSpark, ApacheTez, MachineLearning, ApachePhoenix, ApacheCalcite,ApacheStorm, ApacheAtlas, ApacheKnox, Apache Ranger, HDFS, Apache Pig, Accumulo, Apache Flume, Sqoop, Apache Falcon

Input:

InvokeHttp: I used this to download the first image URL from tweets.

It’s interesting to see this all tie together relatively easily.

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Documenting Tabular Models

Published 2016-10-06 by Kevin Feasel

Meagan Longoria shows how to document a tabular model using Excel and Power BI:

The Database Info tab uses cube functions to display the values shown. The rest of the tabs use pivot tables and slicers.

You can download and use this model if you have Excel 2016 or 2013 with Power Query. Just download the file, change the values in the TabularInstanceName and TabularDBName queries, and refresh. I was able to open the file and update the queries with no issues and no changes in Excel 2013.

This is great if you just need documentation or a quick way to look up specific information. But we could really analyze this using visualizations other than tables in Power BI Desktop.

That’s pretty fancy.  Check it out.

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Running U-SQL Jobs

Published 2016-10-06 by Kevin Feasel

Ginger Grant shows how to run a U-SQL job locally from Visual Studio:

There are a few steps required before any code is run. If the Data Lake Analytics Tools are not installed within Visual Studio, download themhere and install them. When the tools are installed, the menu item Data Lake appears in Visual studio. The second step is to model your PC with the same file structure as your data lake. The default location which the Data Lake tools will look for your data structure is C:\Users\<<insertyourname>>\AppData\Local\USQLDataRoot . What this means is if you have folders and subfolders created in your data lake, your PC needs to have the same structure, including the data.

There is also a way to test these jobs locally before you spend that Azure money spinning up Data Lake jobs.

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Rounding

Published 2016-10-06 by Kevin Feasel

Kenneth Fisher looks at decimal truncation methods:

ROUND

This is the most complicated of the three. It does a standard rounding. If value is .5 or over then you get back 1. If it’s less than .5 you get back 0. On top of that you get to pass the place you want to round to. So for example 0 rounds to the nearest ones place, -1 rounds to the tens place, 2 rounds to the hundredths.

There’s a bonus here:  if you use ROUND in T-SQL, the results are different than the default Round method in .NET; that method uses banker’s rounding by default whereas the T-SQL rounding does not.

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