Today I was trying to create a 2 dimensional data structure that can be queried using string indices rather than integer ones, this is using Python which am a total newbie in (but trying to write a research project using).

The idea was to find something natively within Python, rather than implement my own structure, such a data-structure is fundamental in programming theory, so a very likely chance that an out-of-the-box implementation exists already in most languages, and they are generally a dimensional extension of Arrays (Array of Array), Lists (List of Lists) or Dictionaries (Dictionary of Dictionary), but with string rather than integer indexes.

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Trend analysis in my experience is generally done through manual (human) review and exploration of data through various BI tools, these tools do a great job by visually highlighting data that can be of interest to the data analyst, and when coupled with data-mining techniques such as clustering and forecasting, it gives us invaluable and actionable information that can help us further explore and exploit the business or data model at hand. As far as I can tell, the name of the game these days is “exploratory data analysis and mining”, at least in terms of Business Intelligence products on the market and the direction they are taking.

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There are a few ways you could grant a user execution permission on stored procedures, through assigning permissions on different object hierarchies (objects/schema/database) you can control the level of permissions to ensure optimum security and flexibility.

This post will go through how to grant SQL Server execution permissions on individual stored procedure objects within a database, how to grant execution permission on all object within a schema (including functions), and finally how to grant execution permission across the whole database.

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SQL Server provides Full-Text search capabilities through it’s Full-Text Index, a mature document search tool with neat features like thesaurus and stop-word integration as well as some semantic search and keyword extraction features in SQL Server 2012.

The Full-Text Index is used through 2 (scalar) functions CONTAINS and FREETEXT, and 2 (table-valued) functions CONTAINSTABLE and FREETEXTTABLE. In this post I will be briefly exploring the difference between each of those functions.

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SQL Server uses two methods to ensure transactional consistency and protects the data that is being accessed, these are Locks and Row-Versioning, these methods ensure that you manage your data concurrency effectively by specifying the level of access other transactions have to the data being processed, the game here is to balance either resources or data integrity against concurrency.

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SQL Server queries can be returned as a valid XML as well as a rowset (by default), there are multiple ways you could control the format the end result XML is returned in, giving SQL developers flexibility that can meet most of the demand of an application consuming this XML data. XML support has been natively built into SQL Server since the 2005 release.

In this post I will be briefly exploring the different type of XML output modes supported by SQL Server through the FOR XML statement.

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NLTK (Natural Language Toolkit) is a Python library that allows developers and researchers to extract information and annotations from text, and run classification algorithms such as the Naive Bayes or Maximum Entropy, as well as many other interesting Natural Language tools and processing techniques.

The Maximum Entropy algorithm from NLTK comes in different flavours, this post will introduce the different Max Ent classification algorithm flavours supported by the NLTK library, as well as provide a compiled MEGAM binary on a Linux (Ubuntu) 64-bit machine, which is a requirement for running Max Ent NLTK classification on the megam algorithm.

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There has been a lot of buzz on Twitter going around today about Microsoft’s new Mobile BI Platform, dubbed Project Helix, which was unveiled at Microsoft’s SharePoint event last week.

The screenshots originated from Just Blindbaek, and shows some aspects of the new Mobile BI Platform that is due to be released in 2013, if Microsoft’s Mobile BI roadmap is still accurate.

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Being able to holistically view your SQL Server Job Agent job schedules, how long each job took to run, what other jobs were running at the same time and the status of each job, should be a fundamental part of Job Agent management.

In this post I will be briefly introducing the current SSRS reports integrated into SSMS (or SQL Data Tools) that displays SQL Server Job Agent job information, as well as provide a new report (pictures above) that should give you a better holistic view of activities on your SQL Server Agent, as well as highlight any conflict in job schedules or times were no activity is occurring.

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There are multiple ways to access SQL Job Agent jobs execution statistics, such as:

• Through SQL Server Management Objects (SMO): which provide a programmatic way of accessing and controlling many SQL Server objects. The Smo.Agent namespace will provide the required objects for accessing job statistics such as the last time each job (or step) ran, the duration the job ran for and the returned status as well as many other relevant information regarding job execution.
• Through undocumented Stored Procedures such as master.dbo.xp_sqlagent_enum_jobs: Although this doesn’t provide a lot of flexibility, particularly if you are executing and consuming the data within the context of T-SQL, rather than programmatically through wrapper classes that simplifies handling stored procedures with multiple output tables. It is important to note that since this stored procedure is undocumented, Microsoft could potentially retire it without prior notice, leaving your code non-functional.
• Through Job Agent System Tables: In my opinion this provides the best and most supported method of access. for the rest of this post I will be discussing the different System Tables provided to access SQL Server Job Agent statistics and execution information.