I’ve written previously about development of Tableau analytics capability from single user to multiple teams across an organization. This article is intended for those who may have first installed Tableau Server to enable folks outside their own sphere to interact with their Tableau creations. For the way ahead, it presents a few guidelines for successful development and deployment that data analysts should internalize as their analytics product grows.
The theme is, from the very start, to develop dashboards as if they serve hundreds of users and access millions of data records. If you do that, then as your analytical tools grow in usefulness and popularity, you’ll avoid difficult conversions and retooling later. Continue reading →
Frequently in my career I’ve selected or helped select ETL and reporting professionals who need SQL skills. For some of those opportunities, placement firms returned resumes with interminable, and nearly identical, lists of technical achievements with excruciating unnecessary detail (paraphrasing: “Wrote SELECT statements using GROUP BY”, “Applied both inner and outer joins”). Before interviewing we typically ranked candidates in order of preference based on resumes. Candidates’ interview success bore little relation to resume-based rankings.
With some candidates I’ve encountered consistent pauses after fact questions, sometimes accompanied by keyboard clicks. They obviously used “think time” to look up answers on the internet. As a result, “fact” questions didn’t distinguish one candidate from another.
On the other hand, open ended questions worked well. I’ve written before that interviews should ask opinion rather than fact questions. Open ended questions or thought exercise, as opposed to fact questions, assess SQL skill level, are hard to quickly look up on the web, and have the added benefit of demonstrating a candidate’s reasoning and communication skills. Here are two of my favorite examples: Continue reading →
Not too long ago I posted on how to avoid the dreaded “No more spool space” error in Teradata SQL. That post recounted approaches to restructuring SQL queries so that they would avoid being cancelled for using inordinate amounts of Teradata resources. Teradata is an immensely powerful, even if aging, database engine but it does little to help one not steeped in knowledge of its structure to use its resources efficiently.
But what if, as sometimes happens, your DB admin team further tightens the screws by reducing spool space, or imposing new execution time or CPU usage limits? Then, you’ll have to go further to make queries efficient, as happened on one team that I was a part of. Beyond the steps previously recommended, here’s what we did: Continue reading →
If you are a SQL developer or data analyst working with Teradata, it is likely you’ve gotten this error message: “Select Failed.  No more spool space”. Roughly speaking, Teradata “spool” is the space DBAs assign to each user account as work space for queries. So, for example, if your query needs to build an intermediate table behind the scenes to sort or otherwise process before it hands over your result set, that happens in spool space. It is limited, in part, to keep your potentially runaway query from using up too much space and clogging up the system.
After briefly setting the stage, this post presents the top three tactics I use to avoid or overcome spool space errors. For the second two tactics I’ll show working code. At the end of the post you’ll find volatile DDL that you can use to get the queries to run. Continue reading →
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