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| | We Don't Have Perfect Information I was discussing the PASS Summit with someone and they were wondering about building their schedule. Actually, they wanted to pick sessions, but see the choices in a calendar format, but the schedule wasn't out at that point in time. My suggestion was to just build the schedule and then sort out conflicts later. A few people have mentioned over the years that they want to build a schedule and be ready for the event to maximize their experience and be efficient. I think that's a common, normal thing that many technical people like to do. Many of us are Type-A, and we like knowing our plans and having a schedule prepared in advance. We can then decide which sessions might have greater priority for us during a time slow and adjust our choices to have the best experience. After all, we may want to see two speakers, but if they present at the same time, we have to make a choice. The problem is that we don't have perfect information. Even if the descriptions and abstracts included perfect information about the agendas, what is covered, and to what depth, including demos, we'd still not necessarily assimilate and recognize all that data in a way that makes sense, even to us. There are no shortage of people that make plans today and are unhappy a few weeks later. Even if we knew what we wanted to watch, many of us might think a session on database design has to cover third normal form, even when the text said this examines PKs and FKs. We might assume an SSIS data load talk included something on CSVs when the presenter described the talk as being for ragged right text files. We're human, and that means we have flaws in how we deal with the world. This includes the ways in which we model and analyze data. We can make mistakes in our analysis often when we simplify our view of a problem to the point where our analysis is inherently flawed. If we don't account for this and assume we're flawed, we may overweight our conclusions. I try to remember this when I write reports from systems that others will use. I won't have every piece of information that might affect a decision, but I try to ensure I have the most important, or significant, data. Or at least, the data I and the users feel is significant. The important thing to remember is that out data is always incomplete, and it's entirely possible that we have missed a valuable piece of data. When that happens, we have to adapt and adjust our report, our application, or our conference schedule. We'll learn more across time and we can use that information to change our system. I know that my view of a conference like the PASS Summit today, or even a week before the event, will be different than how I feel at the event. I should have a plan, but be willing to flex as circumstances change. And, always have a backup. I like to pick two or three sessions for every time slot, just in case. Steve Jones from SQLServerCentral.comJoin the debate, and respond to today's editorial on the forums |
| The Voice of the DBA Podcast Listen to the MP3 Audio ( 5.3MB) podcast or subscribe to the feed at iTunes and Libsyn. The Voice of the DBA podcast features music by Everyday Jones. No relation, but I stumbled on to them and really like the music. | |
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| | | Thomas LeBlanc from SQLServerCentral.com In this first level of the SSAS Tabular stairway, learn how to implement and format a date dimension. More » |
| Press Release from Redgate In this free eBook, Kalen Delaney explains how Microsoft's 2016 In-memory OLTP engine works. In her book, learn how to use lock- and latch-free data structures to allow non-blocking data processing, and find out how to migrate existing tables to Hekaton. More » |
| Additional Articles from MSSQLTips.com This article looks at how Microsoft implemented the Python language support into SQL Server and how it can be used. More » |
| Klaus Aschenbrenner from SQLServerCentral Blogs (Be sure to checkout the FREE SQLpassion Performance Tuning Training Plan - you get a weekly email packed with all the... More » |
| Andy Galbraith from SQLServerCentral Blogs One of the items I frequently deal with as a Production DBA is drive alarms. I have written previously about... More » |
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| | Today's Question (by Steve Jones): I have created a few vectors in R with this code: > west.teams = c('Diamondbacks', 'Rockies', 'Dodgers', 'Giants', 'Padres') > west.wins = c(72,71,70,65,50) > west.losses = c(58,59,61,67,83) I want to combine these vectors into a data frame. How do I do this? |
Think you know the answer? Click here, and find out if you are right. We keep track of your score to give you bragging rights against your peers. This question is worth 1 point in this category: R Language. We'd love to give you credit for your own question and answer. To submit a QOTD, simply log in to the Contribution Center. |
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| Yesterday's Question of the Day |
| Yesterday's Question (by Steve Jones): With this table CREATE TABLE UserConfig ( UserConfigKey INT IDENTITY(1,1) NOT NULL CONSTRAINT UserConfigPK PRIMARY KEY , UserID INT , IsActive BIT SPARSE , IsSubscriber BIT SPARSE , DefaultQuantity INT SPARSE , Options XML COLUMN_SET FOR ALL_SPARSE_COLUMNS ) GO We run this query INSERT dbo.UserConfig ( UserID , IsActive , IsSubscriber , DefaultQuantity ) VALUES (8, 1, 1, 5) and then this SELECT * FROM dbo.UserConfig AS uc WHERE uc.UserConfigKey = 8 ; What is returned? Answer: The UserConfigKey, the UserID, and the Options column with 3 XML nodes. Explanation: The Options column is a column set. As values are added to the sparse columns, the column set returns an XML representation of the values. Ref: Use Column Sets - click here » Discuss this question and answer on the forums |
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| Database Pros Who Need Your Help |
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