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  • Accessing SQL Server data from iOS apps

    - by RobertChipperfield
    Almost all mobile apps need access to external data to be valuable. With a huge amount of existing business data residing in Microsoft SQL Server databases, and an ever-increasing drive to make more and more available to mobile users, how do you marry the rather separate worlds of Microsoft's SQL Server and Apple's iOS devices? The classic answer: write a web service layer Look at any of the questions on this topic asked in Internet discussion forums, and you'll inevitably see the answer, "just write a web service and use that!". But what does this process gain? For a well-designed database with a solid security model, and business logic in the database, writing a custom web service on top of this just to access some of the data from a different platform seems inefficient and unnecessary. Desktop applications interact with the SQL Server directly - why should mobile apps be any different? The better answer: the iSql SDK Working along the lines of "if you do something more than once, make it shared," we set about coming up with a better solution for the general case. And so the iSql SDK was born: sitting between SQL Server and your iOS apps, it provides the simple API you're used to if you've been developing desktop apps using the Microsoft SQL Native Client. It turns out a web service remained a sensible idea: HTTP is much more suited to the Big Bad Internet than SQL Server's native TDS protocol, removing the need for complex configuration, firewall configuration, and the like. However, rather than writing a web service for every app that needs data access, we made the web service generic, serving only as a proxy between the SQL Server and a client library integrated into the iPhone or iPad app. This client library handles all the network communication, and provides a clean API. OSQL in 25 lines of code As an example of how to use the API, I put together a very simple app that allowed the user to enter one or more SQL statements, and displayed the results in a rather primitively formatted text field. The total amount of Objective-C code responsible for doing the work? About 25 lines. You can see this in action in the demo video. Beta out now - your chance to give us your suggestions! We've released the iSql SDK as a beta on the MobileFoo website: you're welcome to download a copy, have a play in your own apps, and let us know what we've missed using the Feedback button on the site. Software development should be fun and rewarding: no-one wants to spend their time writing boiler-plate code over and over again, so stop writing the same web service code, and start doing exciting things in the new world of mobile data!

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  • How to make sure you see the truth with Management Studio

    - by fatherjack
    LiveJournal Tags: TSQL,How To,SSMS,Tips and Tricks Did you know that SQL Server Management Studio can mislead you with how your code is performing? I found a query that was using a scalar function to return a date and wanted to take the opportunity to remove it in favour of a table valued function that would be more efficient. The original function was simply returning the start date of the current financial year. The code we were using was: ALTER  FUNCTION...(read more)

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  • TSQL formatting - a sure fire way to start a conversation.

    - by fatherjack
    There are probably as many opinions on ways to format code as there are people writing code and I am not here to say that any one is better than any other. Well, that isn't true. I am here to say that one way is better than another but this isn't a matter of preference or personal taste, this is an example of where sloppy formatting can cause TSQL to weird and whacky things but following some simple methods can make your code more reliable and more robust when . Take these two pieces of code, ready...(read more)

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  • Speakers, Please Check Your Time

    - by AjarnMark
    Woodrow Wilson was once asked how long it would take him to prepare for a 10 minute speech. He replied "Two weeks". He was then asked how long it would take for a 1 hour speech. "One week", he replied. 2 hour speech? "I'm ready right now," he replied.  Whether that is a true story or an urban legend, I don’t really know, but either way, it is a poignant reminder for all speakers, and particularly apropos this week leading up to the PASS Community Summit. (Cross-posted to the PASS Professional Development Virtual Chapter blog #PASSProfDev.) What’s the point of that story?  Simply this…if you have plenty of time to do your presentation, you don’t need to prepare much because it is easy to throw in more and more material to stretch out to your allotted time.  But if you are on a tight time constraint, then it will take significant preparation to distill your talk down to only the essential points. I have attended seven of the last eight North American Summit events, and every one of them has been fantastic.  The speakers are great, the material is timely and relevant, and the networking opportunities are awesome.  And every year, there is one little thing that just bugs me…speakers going over their allotted time.  Why does it bother me so?  Well, if you look at a typical schedule for a Summit, you’ll see that there are six or more sessions going on at the same time, and only 15 minutes to move from one to another.  If you’re trying to maximize your training dollar by attending something during every session time slot, and you don’t want to be the last guy trying to squeeze into the middle of the row, then those 15 minutes can be critical.  All the more so if you need to stop and use the bathroom or if you have to hike to the opposite end of the convention center.  It is really a bad position to find yourself having to choose between learning the last key points of Speaker A who is going over time, and getting over to Speaker B on time so you don’t miss her key opening remarks. And frankly, I think it is just rude.  Yes, the speakers are the function, after all they are bringing the content that the rest of us are paying to learn.  But it is also an honor to be given the opportunity to speak at a conference like this, and no one speaker is so important that the conference would be a disaster without him.  Speakers know when they submit their abstract, long before the conference, how much time they will have.  It has been the same pattern at the Summit for at least the last eight years.  Program Sessions are 75 minutes long.  Some speakers who have a good track record, and meet other qualifying criteria, are extended an invitation to present a Spotlight Session which is 90 minutes (a 20% increase).  So there really is no excuse.  It’s not like you were promised a 2-hour segment and then discovered when you got here that it was only 75 minutes.  In fact, it’s not like PASS advertised 90-minute sessions for everyone and then a select few were cut back to only 75.  As a speaker, you know well before you get here which type of session you are doing and how long it is, so as a professional, you should plan accordingly. Now you might think that this only happens to rookies, but I’ll tell you that some of the worst offenders are big-name veterans who draw huge attendance numbers for their sessions.  Some attendees blow this off as, “Hey, it’s so-and-so, and I’d stay here for hours and listen to him/her talk.”  To which I would reply, “Then they should have submitted for a pre- or post-conference day-long seminar instead, but don’t try to squeeze your day-long talk into a 90-minute session.”  Now I don’t really believe that these speakers are being malicious or just selfishly trying to extend their time in the spotlight.  I think that most of them are merely being undisciplined and did not trim their presentation sufficiently, or allowed themselves to get off-track (often in a generous attempt to help someone in the audience with a question or problem that really should have been noted for further discussion after the session). So here is my recommendation…my plea, even.  TRIM THE FAT!  Now.  Before it’s too late.  Before you even get on the airplane, take a long, hard look at your presentation and eliminate some of the points that you originally thought you had to make, but in reality are not truly crucial to your main topic.  Delete a few slides.  Test your demos and have them already scripted rather than typing them during your talk.  It is better to cut out too much and end up with plenty of time at the end for Questions & Answers.  And you can always keep some notes on the stuff that you cut out so that you could fill it back in at the end as bonus material if you really do end up with a whole bunch of time on your hands.  But I don’t think you will.  And if you do, that will look even better to the audience as it will look like you’re giving them something extra that not every audience gets.  And they will thank you for that.

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  • Documentation and Test Assertions in Databases

    - by Phil Factor
    When I first worked with Sybase/SQL Server, we thought our databases were impressively large but they were, by today’s standards, pathetically small. We had one script to build the whole database. Every script I ever read was richly annotated; it was more like reading a document. Every table had a comment block, and every line would be commented too. At the end of each routine (e.g. procedure) was a quick integration test, or series of test assertions, to check that nothing in the build was broken. We simply ran the build script, stored in the Version Control System, and it pulled everything together in a logical sequence that not only created the database objects but pulled in the static data. This worked fine at the scale we had. The advantage was that one could, by reading the source code, reach a rapid understanding of how the database worked and how one could interface with it. The problem was that it was a system that meant that only one developer at the time could work on the database. It was very easy for a developer to execute accidentally the entire build script rather than the selected section on which he or she was working, thereby cleansing the database of everyone else’s work-in-progress and data. It soon became the fashion to work at the object level, so that programmers could check out individual views, tables, functions, constraints and rules and work on them independently. It was then that I noticed the trend to generate the source for the VCS retrospectively from the development server. Tables were worst affected. You can, of course, add or delete a table’s columns and constraints retrospectively, which means that the existing source no longer represents the current object. If, after your development work, you generate the source from the live table, then you get no block or line comments, and the source script is sprinkled with silly square-brackets and other confetti, thereby rendering it visually indigestible. Routines, too, were affected. In our system, every routine had a directly attached string of unit-tests. A retro-generated routine has no unit-tests or test assertions. Yes, one can still commit our test code to the VCS but it’s a separate module and teams end up running the whole suite of tests for every individual change, rather than just the tests for that routine, which doesn’t scale for database testing. With Extended properties, one can get the best of both worlds, and even use them to put blame, praise or annotations into your VCS. It requires a lot of work, though, particularly the script to generate the table. The problem is that there are no conventional names beyond ‘MS_Description’ for the special use of extended properties. This makes it difficult to do splendid things such ensuring the integrity of the build by running a suite of tests that are actually stored in extended properties within the database and therefore the VCS. We have lost the readability of database source code over the years, and largely jettisoned the use of test assertions as part of the database build. This is not unexpected in view of the increasing complexity of the structure of databases and number of programmers working on them. There must, surely, be a way of getting them back, but I sometimes wonder if I’m one of very few who miss them.

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  • Data Model Dissonance

    - by Tony Davis
    So often at the start of the development of database applications, there is a premature rush to the keyboard. Unless, before we get there, we’ve mapped out and agreed the three data models, the Conceptual, the Logical and the Physical, then the inevitable refactoring will dog development work. It pays to get the data models sorted out up-front, however ‘agile’ you profess to be. The hardest model to get right, the most misunderstood, and the one most neglected by the various modeling tools, is the conceptual data model, and yet it is critical to all that follows. The conceptual model distils what the business understands about itself, and the way it operates. It represents the business rules that govern the required data, its constraints and its properties. The conceptual model uses the terminology of the business and defines the most important entities and their inter-relationships. Don’t assume that the organization’s understanding of these business rules is consistent or accurate. Too often, one department has a subtly different understanding of what an entity means and what it stores, from another. If our conceptual data model fails to resolve such inconsistencies, it will reduce data quality. If we don’t collect and measure the raw data in a consistent way across the whole business, how can we hope to perform meaningful aggregation? The conceptual data model has more to do with business than technology, and as such, developers often regard it as a worthy but rather arcane ceremony like saluting the flag or only eating fish on Friday. However, the consequences of getting it wrong have a direct and painful impact on many aspects of the project. If you adopt a silo-based (a.k.a. Domain driven) approach to development), you are still likely to suffer by starting with an incomplete knowledge of the domain. Even when you have surmounted these problems so that the data entities accurately reflect the business domain that the application represents, there are likely to be dire consequences from abandoning the goal of a shared, enterprise-wide understanding of the business. In reading this, you may recall experiences of the consequence of getting the conceptual data model wrong. I believe that Phil Factor, for example, witnessed the abandonment of a multi-million dollar banking project due to an inadequate conceptual analysis of how the bank defined a ‘customer’. We’d love to hear of any examples you know of development projects poleaxed by errors in the conceptual data model. Cheers, Tony

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  • Oh no! My padding's invalid!

    - by Simon Cooper
    Recently, I've been doing some work involving cryptography, and encountered the standard .NET CryptographicException: 'Padding is invalid and cannot be removed.' Searching on StackOverflow produces 57 questions concerning this exception; it's a very common problem encountered. So I decided to have a closer look. To test this, I created a simple project that decrypts and encrypts a byte array: // create some random data byte[] data = new byte[100]; new Random().NextBytes(data); // use the Rijndael symmetric algorithm RijndaelManaged rij = new RijndaelManaged(); byte[] encrypted; // encrypt the data using a CryptoStream using (var encryptor = rij.CreateEncryptor()) using (MemoryStream encryptedStream = new MemoryStream()) using (CryptoStream crypto = new CryptoStream( encryptedStream, encryptor, CryptoStreamMode.Write)) { crypto.Write(data, 0, data.Length); encrypted = encryptedStream.ToArray(); } byte[] decrypted; // and decrypt it again using (var decryptor = rij.CreateDecryptor()) using (CryptoStream crypto = new CryptoStream( new MemoryStream(encrypted), decryptor, CryptoStreamMode.Read)) { byte[] decrypted = new byte[data.Length]; crypto.Read(decrypted, 0, decrypted.Length); } Sure enough, I got exactly the same CryptographicException when trying to decrypt the data even in this simple example. Well, I'm obviously missing something, if I can't even get this single method right! What does the exception message actually mean? What am I missing? Well, after playing around a bit, I discovered the problem was fixed by changing the encryption step to this: // encrypt the data using a CryptoStream using (var encryptor = rij.CreateEncryptor()) using (MemoryStream encryptedStream = new MemoryStream()) { using (CryptoStream crypto = new CryptoStream( encryptedStream, encryptor, CryptoStreamMode.Write)) { crypto.Write(data, 0, data.Length); } encrypted = encryptedStream.ToArray(); } Aaaah, so that's what the problem was. The CryptoStream wasn't flushing all it's data to the MemoryStream before it was being read, and closing the stream causes it to flush everything to the backing stream. But why does this cause an error in padding? Cryptographic padding All symmetric encryption algorithms (of which Rijndael is one) operates on fixed block sizes. For Rijndael, the default block size is 16 bytes. This means the input needs to be a multiple of 16 bytes long. If it isn't, then the input is padded to 16 bytes using one of the padding modes. This is only done to the final block of data to be encrypted. CryptoStream has a special method to flush this final block of data - FlushFinalBlock. Calling Stream.Flush() does not flush the final block, as you might expect. Only by closing the stream or explicitly calling FlushFinalBlock is the final block, with any padding, encrypted and written to the backing stream. Without this call, the encrypted data is 16 bytes shorter than it should be. If this final block wasn't written, then the decryption gets to the final 16 bytes of the encrypted data and tries to decrypt it as the final block with padding. The end bytes don't match the padding scheme it's been told to use, therefore it throws an exception stating what is wrong - what the decryptor expects to be padding actually isn't, and so can't be removed from the stream. So, as well as closing the stream before reading the result, an alternative fix to my encryption code is the following: // encrypt the data using a CryptoStream using (var encryptor = rij.CreateEncryptor()) using (MemoryStream encryptedStream = new MemoryStream()) using (CryptoStream crypto = new CryptoStream( encryptedStream, encryptor, CryptoStreamMode.Write)) { crypto.Write(data, 0, data.Length); // explicitly flush the final block of data crypto.FlushFinalBlock(); encrypted = encryptedStream.ToArray(); } Conclusion So, if your padding is invalid, make sure that you close or call FlushFinalBlock on any CryptoStream performing encryption before you access the encrypted data. Flush isn't enough. Only then will the final block be present in the encrypted data, allowing it to be decrypted successfully.

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  • Software Tuned to Humanity

    - by Phil Factor
    I learned a great deal from a cynical old programmer who once told me that the ideal length of time for a compiler to do its work was the same time it took to roll a cigarette. For development work, this is oh so true. After intently looking at the editing window for an hour or so, it was a relief to look up, stretch, focus the eyes on something else, and roll the possibly-metaphorical cigarette. This was software tuned to humanity. Likewise, a user’s perception of the “ideal” time that an application will take to move from frame to frame, to retrieve information, or to process their input has remained remarkably static for about thirty years, at around 200 ms. Anything else appears, and always has, to be either fast or slow. This could explain why commercial applications, unlike games, simulations and communications, aren’t noticeably faster now than they were when I started programming in the Seventies. Sure, they do a great deal more, but the SLAs that I negotiated in the 1980s for application performance are very similar to what they are nowadays. To prove to myself that this wasn’t just some rose-tinted misperception on my part, I cranked up a Z80-based Jonos CP/M machine (1985) in the roof-space. Within 20 seconds from cold, it had loaded Wordstar and I was ready to write. OK, I got it wrong: some things were faster 30 years ago. Sure, I’d now have had all sorts of animations, wizzy graphics, and other comforting features, but it seems a pity that we have used all that extra CPU and memory to increase the scope of what we develop, and the graphical prettiness, but not to speed the processes needed to complete a business procedure. Never mind the weight, the response time’s great! To achieve 200 ms response times on a Z80, or similar, performance considerations influenced everything one did as a developer. If it meant writing an entire application in assembly code, applying every smart algorithm, and shortcut imaginable to get the application to perform to spec, then so be it. As a result, I’m a dyed-in-the-wool performance freak and find it difficult to change my habits. Conversely, many developers now seem to feel quite differently. While all will acknowledge that performance is important, it’s no longer the virtue is once was, and other factors such as user-experience now take precedence. Am I wrong? If not, then perhaps we need a new school of development technique to rival Agile, dedicated once again to producing applications that smoke the rear wheels rather than pootle elegantly to the shops; that forgo skeuomorphism, cute animation, or architectural elegance in favor of the smell of hot rubber. I struggle to name an application I use that is truly notable for its blistering performance, and would dearly love one to do my everyday work – just as long as it doesn’t go faster than my brain.

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  • Connection Strings, an Overview

    We asked Phil to come up with a simple explanation of connection strings. We somehow weren't expecting a 'quote of the day' for your database, or a C# application to gather data from the internet. However, sometimes the oblique approach is the best, especially when the knowledge comes from hard-won experience by a cynical man.

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  • Database Migration Scripts: Getting from place A to place B

    - by Phil Factor
    We’ll be looking at a typical database ‘migration’ script which uses an unusual technique to migrate existing ‘de-normalised’ data into a more correct form. So, the book-distribution business that uses the PUBS database has gradually grown organically, and has slipped into ‘de-normalisation’ habits. What’s this? A new column with a list of tags or ‘types’ assigned to books. Because books aren’t really in just one category, someone has ‘cured’ the mismatch between the database and the business requirements. This is fine, but it is now proving difficult for their new website that allows searches by tags. Any request for history book really has to look in the entire list of associated tags rather than the ‘Type’ field that only keeps the primary tag. We have other problems. The TypleList column has duplicates in there which will be affecting the reporting, and there is the danger of mis-spellings getting there. The reporting system can’t be persuaded to do reports based on the tags and the Database developers are complaining about the unCoddly things going on in their database. In your version of PUBS, this extra column doesn’t exist, so we’ve added it and put in 10,000 titles using SQL Data Generator. /* So how do we refactor this database? firstly, we create a table of all the tags. */IF  OBJECT_ID('TagName') IS NULL OR OBJECT_ID('TagTitle') IS NULL  BEGIN  CREATE TABLE  TagName (TagName_ID INT IDENTITY(1,1) PRIMARY KEY ,     Tag VARCHAR(20) NOT NULL UNIQUE)  /* ...and we insert into it all the tags from the list (remembering to take out any leading spaces */  INSERT INTO TagName (Tag)     SELECT DISTINCT LTRIM(x.y.value('.', 'Varchar(80)')) AS [Tag]     FROM     (SELECT  Title_ID,          CONVERT(XML, '<list><i>' + REPLACE(TypeList, ',', '</i><i>') + '</i></list>')          AS XMLkeywords          FROM   dbo.titles)g    CROSS APPLY XMLkeywords.nodes('/list/i/text()') AS x ( y )  /* we can then use this table to provide a table that relates tags to articles */  CREATE TABLE TagTitle   (TagTitle_ID INT IDENTITY(1, 1),   [title_id] [dbo].[tid] NOT NULL REFERENCES titles (Title_ID),   TagName_ID INT NOT NULL REFERENCES TagName (Tagname_ID)   CONSTRAINT [PK_TagTitle]       PRIMARY KEY CLUSTERED ([title_id] ASC, TagName_ID)       ON [PRIMARY])        CREATE NONCLUSTERED INDEX idxTagName_ID  ON  TagTitle (TagName_ID)  INCLUDE (TagTitle_ID,title_id)        /* ...and it is easy to fill this with the tags for each title ... */        INSERT INTO TagTitle (Title_ID, TagName_ID)    SELECT DISTINCT Title_ID, TagName_ID      FROM        (SELECT  Title_ID,          CONVERT(XML, '<list><i>' + REPLACE(TypeList, ',', '</i><i>') + '</i></list>')          AS XMLkeywords          FROM   dbo.titles)g    CROSS APPLY XMLkeywords.nodes('/list/i/text()') AS x ( y )    INNER JOIN TagName ON TagName.Tag=LTRIM(x.y.value('.', 'Varchar(80)'))    END    /* That's all there was to it. Now we can select all titles that have the military tag, just to try things out */SELECT Title FROM titles  INNER JOIN TagTitle ON titles.title_ID=TagTitle.Title_ID  INNER JOIN Tagname ON Tagname.TagName_ID=TagTitle.TagName_ID  WHERE tagname.tag='Military'/* and see the top ten most popular tags for titles */SELECT Tag, COUNT(*) FROM titles  INNER JOIN TagTitle ON titles.title_ID=TagTitle.Title_ID  INNER JOIN Tagname ON Tagname.TagName_ID=TagTitle.TagName_ID  GROUP BY Tag ORDER BY COUNT(*) DESC/* and if you still want your list of tags for each title, then here they are */SELECT title_ID, title, STUFF(  (SELECT ','+tagname.tag FROM titles thisTitle    INNER JOIN TagTitle ON titles.title_ID=TagTitle.Title_ID    INNER JOIN Tagname ON Tagname.TagName_ID=TagTitle.TagName_ID  WHERE ThisTitle.title_id=titles.title_ID  FOR XML PATH(''), TYPE).value('.', 'varchar(max)')  ,1,1,'')    FROM titles  ORDER BY title_ID So we’ve refactored our PUBS database without pain. We’ve even put in a check to prevent it being re-run once the new tables are created. Here is the diagram of the new tag relationship We’ve done both the DDL to create the tables and their associated components, and the DML to put the data in them. I could have also included the script to remove the de-normalised TypeList column, but I’d do a whole lot of tests first before doing that. Yes, I’ve left out the assertion tests too, which should check the edge cases and make sure the result is what you’d expect. One thing I can’t quite figure out is how to deal with an ordered list using this simple XML-based technique. We can ensure that, if we have to produce a list of tags, we can get the primary ‘type’ to be first in the list, but what if the entire order is significant? Thank goodness it isn’t in this case. If it were, we might have to revisit a string-splitter function that returns the ordinal position of each component in the sequence. You’ll see immediately that we can create a synchronisation script for deployment from a comparison tool such as SQL Compare, to change the schema (DDL). On the other hand, no tool could do the DML to stuff the data into the new table, since there is no way that any tool will be able to work out where the data should go. We used some pretty hairy code to deal with a slightly untypical problem. We would have to do this migration by hand, and it has to go into source control as a batch. If most of your database changes are to be deployed by an automated process, then there must be a way of over-riding this part of the data synchronisation process to do this part of the process taking the part of the script that fills the tables, Checking that the tables have not already been filled, and executing it as part of the transaction. Of course, you might prefer the approach I’ve taken with the script of creating the tables in the same batch as the data conversion process, and then using the presence of the tables to prevent the script from being re-run. The problem with scripting a refactoring change to a database is that it has to work both ways. If we install the new system and then have to rollback the changes, several books may have been added, or had their tags changed, in the meantime. Yes, you have to script any rollback! These have to be mercilessly tested, and put in source control just in case of the rollback of a deployment after it has been in place for any length of time. I’ve shown you how to do this with the part of the script .. /* and if you still want your list of tags for each title, then here they are */SELECT title_ID, title, STUFF(  (SELECT ','+tagname.tag FROM titles thisTitle    INNER JOIN TagTitle ON titles.title_ID=TagTitle.Title_ID    INNER JOIN Tagname ON Tagname.TagName_ID=TagTitle.TagName_ID  WHERE ThisTitle.title_id=titles.title_ID  FOR XML PATH(''), TYPE).value('.', 'varchar(max)')  ,1,1,'')    FROM titles  ORDER BY title_ID …which would be turned into an UPDATE … FROM script. UPDATE titles SET  typelist= ThisTaglistFROM     (SELECT title_ID, title, STUFF(    (SELECT ','+tagname.tag FROM titles thisTitle      INNER JOIN TagTitle ON titles.title_ID=TagTitle.Title_ID      INNER JOIN Tagname ON Tagname.TagName_ID=TagTitle.TagName_ID    WHERE ThisTitle.title_id=titles.title_ID    ORDER BY CASE WHEN tagname.tag=titles.[type] THEN 1 ELSE 0  END DESC    FOR XML PATH(''), TYPE).value('.', 'varchar(max)')    ,1,1,'')  AS ThisTagList  FROM titles)fINNER JOIN Titles ON f.title_ID=Titles.title_ID You’ll notice that it isn’t quite a round trip because the tags are in a different order, though we’ve managed to make sure that the primary tag is the first one as originally. So, we’ve improved the database for the poor book distributors using PUBS. It is not a major deal but you’ve got to be prepared to provide a migration script that will go both forwards and backwards. Ideally, database refactoring scripts should be able to go from any version to any other. Schema synchronization scripts can do this pretty easily, but no data synchronisation scripts can deal with serious refactoring jobs without the developers being able to specify how to deal with cases like this.

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  • 3 tips for SQL Azure connection perfection

    - by Richard Mitchell
    One of my main annoyances when dealing with SQL Azure is of course the occasional connection problems that communicating to a cloud database entails. If you're used to programming against a locally hosted SQL Server box this can be quite a change and annoying like you wouldn't believe. So after hitting the problem again in http://cloudservices.red-gate.com  I thought I'd write a little post to remind myself how I've got it working, I don't say it's right but at least "it works on my machine" Tip...(read more)

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  • Know your Data Lineage

    - by Simon Elliston Ball
    An academic paper without the footnotes isn’t an academic paper. Journalists wouldn’t base a news article on facts that they can’t verify. So why would anyone publish reports without being able to say where the data has come from and be confident of its quality, in other words, without knowing its lineage. (sometimes referred to as ‘provenance’ or ‘pedigree’) The number and variety of data sources, both traditional and new, increases inexorably. Data comes clean or dirty, processed or raw, unimpeachable or entirely fabricated. On its journey to our report, from its source, the data can travel through a network of interconnected pipes, passing through numerous distinct systems, each managed by different people. At each point along the pipeline, it can be changed, filtered, aggregated and combined. When the data finally emerges, how can we be sure that it is right? How can we be certain that no part of the data collection was based on incorrect assumptions, that key data points haven’t been left out, or that the sources are good? Even when we’re using data science to give us an approximate or probable answer, we cannot have any confidence in the results without confidence in the data from which it came. You need to know what has been done to your data, where it came from, and who is responsible for each stage of the analysis. This information represents your data lineage; it is your stack-trace. If you’re an analyst, suspicious of a number, it tells you why the number is there and how it got there. If you’re a developer, working on a pipeline, it provides the context you need to track down the bug. If you’re a manager, or an auditor, it lets you know the right things are being done. Lineage tracking is part of good data governance. Most audit and lineage systems require you to buy into their whole structure. If you are using Hadoop for your data storage and processing, then tools like Falcon allow you to track lineage, as long as you are using Falcon to write and run the pipeline. It can mean learning a new way of running your jobs (or using some sort of proxy), and even a distinct way of writing your queries. Other Hadoop tools provide a lot of operational and audit information, spread throughout the many logs produced by Hive, Sqoop, MapReduce and all the various moving parts that make up the eco-system. To get a full picture of what’s going on in your Hadoop system you need to capture both Falcon lineage and the data-exhaust of other tools that Falcon can’t orchestrate. However, the problem is bigger even that that. Often, Hadoop is just one piece in a larger processing workflow. The next step of the challenge is how you bind together the lineage metadata describing what happened before and after Hadoop, where ‘after’ could be  a data analysis environment like R, an application, or even directly into an end-user tool such as Tableau or Excel. One possibility is to push as much as you can of your key analytics into Hadoop, but would you give up the power, and familiarity of your existing tools in return for a reliable way of tracking lineage? Lineage and auditing should work consistently, automatically and quietly, allowing users to access their data with any tool they require to use. The real solution, therefore, is to create a consistent method by which to bring lineage data from these data various disparate sources into the data analysis platform that you use, rather than being forced to use the tool that manages the pipeline for the lineage and a different tool for the data analysis. The key is to keep your logs, keep your audit data, from every source, bring them together and use the data analysis tools to trace the paths from raw data to the answer that data analysis provides.

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  • Defensive Error Handling

    TRY…CATCH error handling in SQL Server has certain limitations and inconsistencies that will trap the unwary developer, used to the more feature-rich error handling of client-side languages such as C# and Java. In this article, abstracted from his excellent new book, Defensive Database Programming with SQL Server, Alex Kuznetsov offers a simple, robust approach to checking and handling errors in SQL Server, with client-side error handling used to enforce what is done on the server.

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  • Book Review: Defensive Database Programming With SQL Server

    It distils a great deal of practical experience; the writing of it was a considerable task; It packs in a great deal of information. Alex's book shows how to write robust database applications, and we can all learn from it. We took the book to a critic who never minces his words, and were relieved to find that Joe Celko liked it.

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  • Developing Schema Compare for Oracle (Part 6): 9i Query Performance

    - by Simon Cooper
    All throughout the EAP and beta versions of Schema Compare for Oracle, our main request was support for Oracle 9i. After releasing version 1.0 with support for 10g and 11g, our next step was then to get version 1.1 of SCfO out with support for 9i. However, there were some significant problems that we had to overcome first. This post will concentrate on query execution time. When we first tested SCfO on a 9i server, after accounting for various changes to the data dictionary, we found that database registration was taking a long time. And I mean a looooooong time. The same database that on 10g or 11g would take a couple of minutes to register would be taking upwards of 30 mins on 9i. Obviously, this is not ideal, so a poke around the query execution plans was required. As an example, let's take the table population query - the one that reads ALL_TABLES and joins it with a few other dictionary views to get us back our list of tables. On 10g, this query takes 5.6 seconds. On 9i, it takes 89.47 seconds. The difference in execution plan is even more dramatic - here's the (edited) execution plan on 10g: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 108K| 939 || 1 | SORT ORDER BY | | 108K| 939 || 2 | NESTED LOOPS OUTER | | 108K| 938 ||* 3 | HASH JOIN RIGHT OUTER | | 103K| 762 || 4 | VIEW | ALL_EXTERNAL_LOCATIONS | 2058 | 3 ||* 20 | HASH JOIN RIGHT OUTER | | 73472 | 759 || 21 | VIEW | ALL_EXTERNAL_TABLES | 2097 | 3 ||* 34 | HASH JOIN RIGHT OUTER | | 39920 | 755 || 35 | VIEW | ALL_MVIEWS | 51 | 7 || 58 | NESTED LOOPS OUTER | | 39104 | 748 || 59 | VIEW | ALL_TABLES | 6704 | 668 || 89 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2025 | 5 || 106 | VIEW | ALL_PART_TABLES | 277 | 11 |------------------------------------------------------------------------------- And the same query on 9i: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 16P| 55G|| 1 | SORT ORDER BY | | 16P| 55G|| 2 | NESTED LOOPS OUTER | | 16P| 862M|| 3 | NESTED LOOPS OUTER | | 5251G| 992K|| 4 | NESTED LOOPS OUTER | | 4243M| 2578 || 5 | NESTED LOOPS OUTER | | 2669K| 1440 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 ||* 50 | VIEW PUSHED PREDICATE | ALL_TAB_COMMENTS | 2043 | ||* 66 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_TABLES | 1777K| ||* 80 | VIEW PUSHED PREDICATE | ALL_EXTERNAL_LOCATIONS | 1744K| ||* 96 | VIEW | ALL_PART_TABLES | 852K| |------------------------------------------------------------------------------- Have a look at the cost column. 10g's overall query cost is 939, and 9i is 55,000,000,000 (or more precisely, 55,496,472,769). It's also having to process far more data. What on earth could be causing this huge difference in query cost? After trawling through the '10g New Features' documentation, we found item 1.9.2.21. Before 10g, Oracle advised that you do not collect statistics on data dictionary objects. From 10g, it advised that you do collect statistics on the data dictionary; for our queries, Oracle therefore knows what sort of data is in the dictionary tables, and so can generate an efficient execution plan. On 9i, no statistics are present on the system tables, so Oracle has to use the Rule Based Optimizer, which turns most LEFT JOINs into nested loops. If we force 9i to use hash joins, like 10g, we get a much better plan: -------------------------------------------------------------------------------| Id | Operation | Name | Bytes | Cost |-------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 7587K| 3704 || 1 | SORT ORDER BY | | 7587K| 3704 ||* 2 | HASH JOIN OUTER | | 7587K| 822 ||* 3 | HASH JOIN OUTER | | 5262K| 616 ||* 4 | HASH JOIN OUTER | | 2980K| 465 ||* 5 | HASH JOIN OUTER | | 710K| 432 ||* 6 | HASH JOIN OUTER | | 398K| 302 || 7 | VIEW | ALL_TABLES | 342K| 276 || 29 | VIEW | ALL_MVIEWS | 51 | 20 || 50 | VIEW | ALL_PART_TABLES | 852K| 104 || 78 | VIEW | ALL_TAB_COMMENTS | 2043 | 14 || 93 | VIEW | ALL_EXTERNAL_LOCATIONS | 1744K| 31 || 106 | VIEW | ALL_EXTERNAL_TABLES | 1777K| 28 |------------------------------------------------------------------------------- That's much more like it. This drops the execution time down to 24 seconds. Not as good as 10g, but still an improvement. There are still several problems with this, however. 10g introduced a new join method - a right outer hash join (used in the first execution plan). The 9i query optimizer doesn't have this option available, so forcing a hash join means it has to hash the ALL_TABLES table, and furthermore re-hash it for every hash join in the execution plan; this could be thousands and thousands of rows. And although forcing hash joins somewhat alleviates this problem on our test systems, there's no guarantee that this will improve the execution time on customers' systems; it may even increase the time it takes (say, if all their tables are partitioned, or they've got a lot of materialized views). Ideally, we would want a solution that provides a speedup whatever the input. To try and get some ideas, we asked some oracle performance specialists to see if they had any ideas or tips. Their recommendation was to add a hidden hook into the product that allowed users to specify their own query hints, or even rewrite the queries entirely. However, we would prefer not to take that approach; as well as a lot of new infrastructure & a rewrite of the population code, it would have meant that any users of 9i would have to spend some time optimizing it to get it working on their system before they could use the product. Another approach was needed. All our population queries have a very specific pattern - a base table provides most of the information we need (ALL_TABLES for tables, or ALL_TAB_COLS for columns) and we do a left join to extra subsidiary tables that fill in gaps (for instance, ALL_PART_TABLES for partition information). All the left joins use the same set of columns to join on (typically the object owner & name), so we could re-use the hash information for each join, rather than re-hashing the same columns for every join. To allow us to do this, along with various other performance improvements that could be done for the specific query pattern we were using, we read all the tables individually and do a hash join on the client. Fortunately, this 'pure' algorithmic problem is the kind that can be very well optimized for expected real-world situations; as well as storing row data we're not using in the hash key on disk, we use very specific memory-efficient data structures to store all the information we need. This allows us to achieve a database population time that is as fast as on 10g, and even (in some situations) slightly faster, and a memory overhead of roughly 150 bytes per row of data in the result set (for schemas with 10,000 tables in that means an extra 1.4MB memory being used during population). Next: fun with the 9i dictionary views.

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  • Antenna Aligner part 2: Finding the right direction

    - by Chris George
    Last time I managed to get "my first app(tm)" built, published and running on my iPhone. This was really cool, a piece of my code running on my very own device. Ok, so I'm easily pleased! The next challenge was actually trying to determine what it was I wanted this app to do, and how to do it. Reverting back to good old paper and pen, I started sketching out designs for the app. I knew I wanted it to get a list of transmitters, then clicking on a transmitter would display a compass type view, with an arrow pointing the right way. I figured there would not be much point in continuing until I know I could do the graphical part of the project, i.e. the rotating compass, so armed with that reasoning (plus the fact I just wanted to get on and code!), I once again dived into visual studio. Using my friend (google) I found some example code for getting the compass data from the phone using the PhoneGap framework. // onSuccess: Get the current heading // function onSuccess(heading) {    alert('Heading: ' + heading); } navigator.compass.getCurrentHeading(onSuccess, onError); Using the ripple mobile emulator this showed that it was successfully getting the compass heading. But it didn't work when uploaded to my phone. It turns out that the examples I had been looking at were for PhoneGap 1.0, and Nomad uses PhoneGap 1.4.1. In 1.4.1, getCurrentHeading provides a compass object to onSuccess, not just a numeric value, so the code now looks like // onSuccess: Get the current magnetic heading // function onSuccess(heading) {    alert('Heading: ' + heading.magneticHeading); }; navigator.compass.getCurrentHeading(onSuccess, onError); So the lesson learnt from this... read the documentation for the version you are actually using! This does, however, lead to compatibility problems with ripple as it only supports 1.0 which is a real pain. I hope that the ripple system is updated sometime soon.

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  • LiveMeeting VC PowerShell PASS – Troubleshooting SQL Server with PowerShell

    - by Laerte Junior
    Guys, join me on Wednesday July 18th 12 noon EDT (GMT -4) for a presentation called Troubleshooting SQL Server With PowerShell. It will be in English, so please make allowances for this. I’m sure that you’re aware that my English is not perfect, but it is not so bad. I will do my best, you can be sure. The registration link will be available soon from PowerShell.sqlpass.org, so I hope to see you there. It will be a session without slides. Just code; pure PowerShell code. Trust me, We will see a lot of COOL stuff.Big thanks to Aaron Nelson (@sqlvariant) for the opportunity! Here are some more details about the presentation: “Troubleshooting SQL Server with PowerShell – The Next Level’ It is normal for us to have to face poorly performing queries or even complete failure in our SQL server environments. This can happen for a variety of reasons including poor Database Designs, hardware failure, improperly-configured systems and OS Updates applied without testing. As Database Administrators, we need to take precaution to minimize the impact of these problems when they occur, and so we need the tools and methodology required to identify and solve issues quickly. In this Session we will use PowerShell to explore some common troubleshooting techniques used in our day-to-day work as s DBA. This will include a variety of such activities including Gathering Performance Counters in several servers at the same time using background jobs, identifying Blocked Sessions and Reading & filtering the SQL Error Log even if the Instance is offline The approach will be using some advanced PowerShell techniques that allow us to scale the code for multiple servers and run the data collection in asynchronous mode.

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  • New features in SQL Prompt 6.4

    - by Tom Crossman
    We’re pleased to announce a new beta version of SQL Prompt. We’ve been trying out a few new core technologies, and used them to add features and bug fixes suggested by users on the SQL Prompt forum and suggestions forum. You can download the SQL Prompt 6.4 beta here (zip file). Let us know what you think! New features Execute current statement In a query window, you can now execute the SQL statement under your cursor by pressing Shift + F5. For example, if you have a query containing two statements and your cursor is placed on the second statement: When you press Shift + F5, only the second statement is executed:   Insert semicolons You can now use SQL Prompt to automatically insert missing semicolons after each statement in a query. To insert semicolons, go to the SQL Prompt menu and click Insert Semicolons. Alternatively, hold Ctrl and press B then C. BEGIN…END block highlighting When you place your cursor over a BEGIN or END keyword, SQL Prompt now automatically highlights the matching keyword: Rename variables and aliases You can now use SQL Prompt to rename all occurrences of a variable or alias in a query. To rename a variable or alias, place your cursor over an instance of the variable or alias you want to rename and press F2: Improved loading dialog box The database loading dialog box now shows actual progress, and you can cancel loading databases:   Single suggestion improvement SQL Prompt no longer suggests keywords if the keyword has been typed and no other suggestions exist. Performance improvement SQL Prompt now has less impact on Management Studio start up time. What do you think? We want to hear your feedback about the beta. If you have any suggestions, or bugs to report, tell us on the SQL Prompt forum or our suggestions forum.

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  • Monitoring Baseline

    - by Grant Fritchey
    Knowing what's happening on your servers is important, that's monitoring. Knowing what happened on your server is establishing a baseline. You need to do both. I really enjoyed this blog post by Ted Krueger (blog|twitter). It's not enough to know what happened in the last hour or yesterday, you need to compare today to last week, especially if you released software this weekend. You need to compare today to 30 days ago in order to begin to establish future projections. How your data has changed over 30 days is a great indicator how it's going to change for the next 30. No, it's not perfect, but predicting the future is not exactly a science, just ask your local weatherman. Red Gate's SQL Monitor can show you the last week, the last 30 days, the last year, or all data you've collected (if you choose to keep a year's worth of data or more, please have PLENTY of storage standing by). You have a lot of choice and control here over how much data you store. Here's the configuration window showing how you can set this up: This is for version 2.3 of SQL Monitor, so if you're running an older version, you might want to update. The key point is, a baseline simply represents a moment in time in your server. The ability to compare now to then is what you're looking for in order to really have a useful baseline as Ted lays out so well in his post.

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  • Azure

    - by Grant Fritchey
    I've been tasked to learn SQL Azure, as well as test all the Red Gate products on it. My one, BIG, fear has been that I'll receive some mongo bill in the mail because I've exceeded the MSDN testing limit. I know people that have had that problem. I've been trying to keep an eye on my usage, but, let's face it, it's not something I think about every day. But now I don't have to. Red Gate has been working with Azure, long before I showed up. They already released a little piece of software that I just found out about, it's called CloudTally. It gathers your usage and sends you a daily email so you can know if you're starting to approach that limit. Check it out, it's free.

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  • News From EAP Testing

    - by Fatherjack
    There is a phrase that goes something like “Watch the pennies and the pounds/dollars will take care of themselves”, meaning that if you pay attention to the small things then the larger things are going to fare well too. I am lucky enough to be a Friend of Red Gate and once in a while I get told about new features in their tools and have a test copy of the software to trial. I got one of those emails a week or so ago and I have been exploring the SQL Prompt 6 EAP since then. One really useful feature of long standing in SQL Prompt is the idea of a code snippet that is automatically pasted into the SSMS editor when you type a few key letters. For example I can type “ssf” and then press the tab key and the text is expanded to SELECT * FROM. There are lots of these combinations and it is possible to create your own really easily. To create your own you use the Snippet Manager interface to define the shortcut letters and the code that you want to have put in their place. Let’s look at an example. Say I am writing a blog about something and want to have the demo code create a temporary table. It might looks like this; The first time you run the code everything is fine, a lovely set of dates fill the results grid but run it a second time and this happens.   Yep, we didn’t destroy the temporary table so the CREATE statement fails when it finds the table already exists. No matter, I have a snippet created that takes care of this.   Nothing too technical here but you will see that in the Code section there is $CURSOR$, this isn’t a TSQL keyword but a marker for SQL Prompt to place the cursor in that position when the Code is pasted into the SSMS Editor. I just place my cursor above the CREATE statement and type “ifobj” – the shortcut for my code to DROP the temporary table – which has been defined in the Snippet Manager as below. This means I am right-away ready to type the name of the offending table. Pretty neat and it’s been very useful in saving me lots of time over many years.   The news for SQL Prompt 6 is that Red Gate have added a new Snippet Command of $PASTE$. Let’s alter our snippet to the following and try it out   Once again, we will type type “ifobj” in the SSMS Editor but first of all, highlight the name of the table #TestTable and copy it to your clipboard. Now type “ifobj” and press Tab… Wherever the string $PASTE$ is placed in the snippet, the contents of your clipboard are merged into the pasted TSQL. This means I don’t need to type the table name into the code snippet, it’s already there and I am seeing a fully functioning piece of TSQL ready to run. This means it is it even easier to write TSQL quickly and consistently. Attention to detail like this from Red Gate means that their developer tools stay on track to keep winning awards year after year and help take the hard work out of writing neat, accurate TSQL. If you want to try out SQL Prompt all the details are at http://www.red-gate.com/products/sql-development/sql-prompt/.

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  • Comparing Apples and Pairs

    - by Tony Davis
    A recent study, High Costs and Negative Value of Pair Programming, by Capers Jones, pulls no punches in its assessment of the costs-to- benefits ratio of pair programming, two programmers working together, at a single computer, rather than separately. He implies that pair programming is a method rushed into production on a wave of enthusiasm for Agile or Extreme Programming, without any real regard for its effectiveness. Despite admitting that his data represented a far from complete study of the economics of pair programming, his conclusions were stark: it was 2.5 times more expensive, resulted in a 15% drop in productivity, and offered no significant quality benefits. The author provides a more scientific analysis than Jon Evans’ Pair Programming Considered Harmful, but the theme is the same. In terms of upfront-coding costs, pair programming is surely more expensive. The claim of productivity loss is dubious and contested by other studies. The third claim, though, did surprise me. The author’s data suggests that if both the pair and the individual programmers employ static code analysis and testing, then there is no measurable difference in the resulting code quality, in terms of defects per function point. In other words, pair programming incurs a massive extra cost for no tangible return in investment. There were, inevitably, many criticisms of his data and his conclusions, a few of which are persuasive. Firstly, that the driver/observer model of pair programming, on which the study bases its findings, is far from the most effective. For example, many find Ping-Pong pairing, based on use of test-driven development, far more productive. Secondly, that it doesn’t distinguish between “expert” and “novice” pair programmers– that is, independently of other programming skills, how skilled was an individual at pair programming. Thirdly, that his measure of quality is too narrow. This point rings true, certainly at Red Gate, where developers don’t pair program all the time, but use the method in short bursts, while tackling a tricky problem and needing a fresh perspective on the best approach, or more in-depth knowledge in a particular domain. All of them argue that pair programming, and collective code ownership, offers significant rewards, if not in terms of immediate “bug reduction”, then in removing the likelihood of single points of failure, and improving the overall quality and longer-term adaptability/maintainability of the design. There is also a massive learning benefit for both participants. One developer told me how he once worked in the same team over consecutive summers, the first time with no pair programming and the second time pair-programming two-thirds of the time, and described the increased rate of learning the second time as “phenomenal”. There are a great many theories on how we should develop software (Scrum, XP, Lean, etc.), but woefully little scientific research in their effectiveness. For a group that spends so much time crunching other people’s data, I wonder if developers spend enough time crunching data about themselves. Capers Jones’ data may be incomplete, but should cause a pause for thought, especially for any large IT departments, supporting commerce and industry, who are considering pair programming. It certainly shouldn’t discourage teams from exploring new ways of developing software, as long as they also think about how to gather hard data to gauge their effectiveness.

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  • Getting the URL to the Content Type Hub Programmatically in SharePoint 2010

    - by Damon
    Many organizations use the content-type hub to manage content-types in their SharePoint 2010 environment.  As a developer in these types of organizations, you may one day find yourself in need of getting the URL of the content type hub programmatically.  Here is a quick snippet that demonstrates how to do it fairly painlessly: public static Uri GetContentTypeHubUri(SPSite site) {     TaxonomySession session = new TaxonomySession(site);     return Session.DefaultSiteCollectionTermStore         .ContentTypePublishingHub; }

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  • SQL Saturday and Exploring Data Privacy

    - by Johnm
    I have been highly impressed with the growth of the SQL Saturday phenomenon. It seems that an announcement for a new wonderful event finds its way to my inbox on a daily basis. I have had the opportunity to attend the first of the SQL Saturday's for Tampa, Chicago, Louisville and recently my home town of Indianapolis. It is my hope that there will be many more in my future. This past weekend I had the honor of being selected to speak amid a great line up of speakers at SQL Saturday #82 in Indianapolis. My session topic/title was "Exploring Data Privacy". Below is a brief synopsis of my session: Data Privacy in a Nutshell        - Definition of data privacy        - Examples of personally identifiable data        - Examples of Sensitive data Laws and Stuff        - Various examples of laws, regulations and policies that influence the definition of data privacy        - General rules of thumb that encompasses most laws Your Data Footprint        - Who has personal information about you?        - What are you exchanging data privacy for?        - The amazing resilience of data        - The cost of data loss Weapons of Mass Protection       - Data classification       - Extended properties       - Database Object Schemas       - An extraordinarily brief introduction of encryption       - The amazing data professional  <-the most important point of the entire session! The subject of data privacy is one that is quickly making its way to the forefront of the mind of many data professionals. Somewhere out there someone is storing personally identifiable and other sensitive data about you. In some cases it is kept reasonably secure. In other cases it is kept in total exposure without the consideration of its potential of damage to you. Who has access to it and how is it being used? Are we being unnecessarily required to supply sensitive data in exchange for products and services? These are just a few questions on everyone's mind. As data loss events of grand scale hit the headlines in a more frequent succession, the level of frustration and urgency for a solution increases. I assembled this session with the intent to raise awareness of sensitive data and remind us all that we, data professionals, are the ones who have the greatest impact and influence on how sensitive data is regarded and protected. Mahatma Gandhi once said "Be the change you want to see in the world." This is guidance that I keep near to my heart as I approached this topic of data privacy.

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