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Category: T-SQL Tuesday

Appropriate Data Types And Unicode

Published 2018-02-14 by Kevin Feasel

Raul Gonzalez on (in)appropriate use of National character strings:

Yes, you have read it… I see dates stored as NVARCHAR(10) and NCHAR(10) on daily basis, please don’t ask me why.

This case is even worse, because DATE takes 3 bytes where NCHAR(10) takes 20 bytes, yes Ladies and Gentlemen more than 6 times more space to store the same data.

But wait! how can you be certain that those ten characters are actually a valid date? You can’t, unless you reinvent the wheel and validate that those dates are obviously valid dates and pay the performance penalty of doing it.

You’d think that picking the right data type for something would be fairly easy and then you find a table with a few dozen NVARCHAR(MAX) columns.

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T-SQL Tuesday Roundup

Published 2017-11-21 by Kevin Feasel

Ewald Cress has what might have been the largest T-SQL Tuesday ever:

Firstly, I want to thank every person who took part. SIXTY TWO blog posts got generated, including a few first-time #tsql2sday contributors as well as first-time bloggers. I am fairly glowing to have been a part of it, and I hope the other contributors are too.

Secondly, from my own experience in writing a post, I know it feels terrible when you start worrying about who to pick. There are many people I could have included, but I hope I have made my appreciation for them clear elsewhere. Not that I want to speak on your behalf, but I’ll assume that the same applies to many other contributors.

That’s a lot of reading.

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Polybase And HDInsight

Published 2017-10-11 by Kevin Feasel

I have a post up on trying to integrate Polybase with HDInsight:

But now we run into a problem:  there are certain ports which need to be open for Polybase to work.  This includes port 50010 on each of the data nodes against which we want to run MapReduce jobs.  This goes back to the issue we see with spinning up data nodes in Docker:  ports are not available.  If you’ve put your HDInsight cluster into an Azure VNet and monkey around with ports, you might be able to open all of the ports necessary to get this working, but that’s a lot more than I’d want to mess with, as somebody who hasn’t taken the time to learn much about cloud networking.

As I mention in the post, I’d much rather build my own Hadoop cluster; I don’t think you save much maintenance time in the long run going with HDInsight.

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Working With CosmosDB

Published 2017-10-11 by Kevin Feasel

Derik Hammer has an introductory article showing how to work with CosmosDB to store and use document-style data:

Querying Cosmos DB is more powerful and versatile. The CreateDocumentQuery method is used to create an IQueryable<T> object, a member of System.Linq, which can output the query results. The ToList() method will output a List<T> object from the System.Collections.Generic namespace.

Derik also shows how to import the data into Power BI and visualize it.  It’s a nice article if you’ve never played with CosmosDB before.

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The Database Dialectic

Published 2017-10-11 by Kevin Feasel

Rob Farley sees a series of database syntheses, and the Big Data movement is a part of that:

When CLR came in, people said it was a T-SQL killer. I remember a colleague of mine telling me that he didn’t need to learn T-SQL, because CLR meant that he would be able to do it all in .Net. Over time, we’ve learned that CLR is excellent for all kinds of things, but it’s by no means a T-SQL killer. It’s excellent for a number of reasons – CLR stored procedures or functions have been great for things like string splitting and regular expressions – and we’ve learned its place now.

I don’t hear people talking about NoSQL like they once did, and it’s been folded somehow into BigData, but even that seems to have lost a little of its lustre from a year or two ago when it felt like it was ‘all the rage’. And yet we still have data which is “Big”. I don’t mean large, necessarily, just data that satisfies one of the three Vs – volume, velocity, variety.

Rob brings an interesting perspective to the topic, particularly as one of the early Parallel Data Warehouse bloggers.

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The Market For IoT Analytics

Published 2017-10-11 by Kevin Feasel

Kennie Nybo Pontoppidan tells a story for T-SQL Tuesday:

At the time in Rehfeld R&D, we experimented with making Effektor a metadata repository for a Hadoop data warehouse, where instead of generating tables and ETL processes in the different data warehouse layers, the synchronization engine in the product would generate the Hive objects on top of Hadoop tables. We never made more than an overall spec and a prototype, but the experiment gave us some insight into the technologies around Hadoop.

Around that time, Phillips released the Hue lightbulbs, and our COO bought us two packs to play with. The idea was to create a physical BI dashboard, where lightbulbs would display KPIs, and change color according to its value and the KPI threshold. I still think that was a brilliant idea, and I would love to see more use of consumer electronics in enterprise BI.

His basic maturity model makes sense; as much as I really want to disagree with the maturity model, I can’t.  Good read.

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