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A Clock With Benefits

This editorial was originally published on Aug 26, 2015. It is being re-run as Steve is traveling.

One of the difficulties I think that developers face is that so few people understand what actually goes into producing software. The end result that a user sees can mask the effort and complexity of the underlying code. A person might see one application that required months of effort and not understand how it's different than a similar (in their view) application that was prototyped and completed in a week.

I've seen a few efforts to try and explain how code is written, and I think the huge piece by Business Week was a really good explanation for the layman. It tries to simplify concepts, and does so in a humorous way. I suspect that this piece might actually help our clients and customers gain some appreciation for the difficulty of producing a basic application.

However I think we need some additional articles like this to help explain our database world. While there are some good, basic pieces on what a database is, we don't necessarily help anyone understand the complexity of assembling disparate data, especially at scale.

I don't think end users would care much about why database performance can be an issue, but I do think that some well written, easy to read, enjoyable pieces on the issues of writing poor database code might give developers some appreciation for why indexes matter, what RBAR does to performance, and what solutions might help their code shine.

No one wants software written poorly, and no one wants deadlines missed. However the world of software development is complex and the more that we can help our clients understand this, the less time we waste on explanations.

Steve Jones - SSC Editor

Join the debate, and respond to today's editorial on the forums

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  Featured Contents

Cross Database Queries in Azure SQL Database

Steve Jones - SSC Editor from SQLServerCentral

Cross database queries in Azure SQL Database aren't as straightforward as you might think, but they aren't hard to implement. Follow along to learn how to implement them.

Modeling Time

Additional Articles from SimpleTalk

There are those who argue that time doesn’t really exist; it’s just an illusion. That debate won’t keep you from storing and manipulating dates and time in SQL Server. In this article, Joe Celko discusses some of the many ways that SQL Server treats temporal data types.

Static Data and Database Builds

Additional Articles from Redgate

Whichever way you wish to ensure that a database, when built, has all the data that will enable it to function properly, there are reasonably simple ways of doing it. Phil Factor explains the alternatives.

From the SQL Server Central Blogs - Create New Azure VM with PowerShell

Bradley Schacht from Bradley Schacht

Every so often I set out to create new resources in my Azure subscription using the command line tools (PowerShell or CLI) just to keep up to date on...

From the SQL Server Central Blogs - Learning More about Kubernetes (K8s)

Steve Jones - SSC Editor from The Voice of the DBA

I know a bit about this, but I’m always looking for more. I had the chance to see Brendon Burns recently and I was impressed. That is one seriously...


  Question of the Day

Today's question (by Steve Jones - SSC Editor):


Azure VM SQL Database Backups

As a contractor, I have set up a SQL Server instance inside of an Azure VM. I am a little worried about support from the organization that needs this, since they have accidental DBAs that work on the systems. I want to make the backup process simple and easy for them, while ensuring they can have a point in time recovery to an hour. They are comfortable in the Azure Portal, and a solution that works in the Portal is preferred. Which of these should I choose?

Think you know the answer? Click here, and find out if you are right.



  Yesterday's Question of the Day (by Steve Jones - SSC Editor)

Going from long to wide

I have a dataframe of speakers and events across years.

> speaking <- data.frame(ID = 1:3, expand.grid(Name = c("Steve", "Grant", "Kendra"), Year = 2015:2017),
+                        Events = round(runif(9, 18, 35), 0)
+ )

I have the tidyverse library loaded and want to use the wide() command to change my dataframe. I run this.

> widespeaking = speaking %>% spread(Year,Events)

What size and shape of dataframe does this give me?

Answer: 4 columns, 3 rows

Explanation: This will pivot the data on the columns spreading the years out to columns and the events to the cells. There will also be a first column with the names. The results look like:

> widespeaking
  ID   Name 2015 2016 2017
1  1  Steve   30   30   24
2  2  Grant   29   22   34
3  3 Kendra   31   31   27


Discuss this question and answer on the forums




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