In-Memory storage technology finally make their debut appearance on the SQL Server 2014′s BI stack, with the creation of a proper memory optimized tables and stored procedures, unlike the Columnstore feature which offers a read-only memory optimized solution, that does not work overly well in a true transactional environment.

In this post I hope to dissect the new In-Memory tables feature of SQL Server 2014, providing an overview of how the technology works, how to create in-memory tables, maintain them and any pitfalls to watch out from. Mainly though, I am writing this as a reminder to myself of the latest articles I have been reading about this cool new feature.

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Hadoop is a pretty neat set of tools for processing loads of data in a distributed, parallel and easy to scale-out manner, and so rightfully the Hadoop toolset owns a pretty high position in the data analysis and BI game, and a must consider when embarking on any new big data project. But that being said, the Hadoop eco-system, however advance in many areas, is still away from being a complete end to end BI solution, particularly when it comes to offering support for emerging data analysis and business intelligence concepts, such as exploratory data analysis and real-time data querying, or even fully-integrated data visualization and report authoring tools.

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Image by James Shearer

Its a RDBMs, no its a ColumnStore, no its a cloud integrated storage platform… Oh its actually SQL Server 2014!

Exciting news as the new (project code-name: Hekaton) SQL Server 2014 is community technology preview (CTP1) is out and can be downloaded and evaluated by the community.

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Until now, I have found working with Kerberos when setting up a SQL Server stack to be a complete nightmarish experience, mainly due to two reasons:

• Working with Kerberos usually requires access rights to Active Directory for the account setting up this authentication protocol on the stack, in order to be able to effectively diagnose the setup and also configure the Service Principal Names (SPN) for the various SQL Server and SharePoint service accounts, and setup delegation. This means SQL Server architects and Network Administrators need to collaborate in order to correctly configure the stack, which is often an unpleasant and long winded experience of trial and error.
• The lack of a centralized diagnostic and configuration tools for Kerberos setup on SQL Server makes this tasks very tedious, particularly if you follow the limited number of online resources out there to setup Kerberos, and find that they do not apply exactly to your situation, or do not work exactly as intended after following the lengthy steps, and you are left with a very limited option in terms of diagnosing exactly what went wrong.

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Hashing can be a very useful technique when dealing with the storage and look up of large text fields (say a table of URLs or Search Keywords), these fields will incur high resource utilization on any database engine if used directly in DML statements, in which they are either filtered by or aggregated on. Any index built on these fields is costly to maintain, if it is at all possible given that SQL Server limits index size to 900 bytes.

Using hashing functions we can facilitate the handling of large textual data in the relational engine, leading to improved performance when these fields are being compared to satisfy a query, hashing can also be used to build unique and non-unique indexes that are easier to manage than directly using the text fields in the index definition. In this post we will discuss a few options for hashing large text data using functions native within SQL Server, as well as provide other external  hashing algorithms that we can integrate into Microsoft’s SQL Server (or any RDBMS for that matter) that might provide a better practical performance. 

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On one corner we have Hadoop, a massively distributed JVM-based data processing engine with a Map & Reduce API and a proven track record in handling huge data-sets. On the other corner we have SSIS, a natively non-distributed ETL engine part of the SQL Server family tool-set with .NET code extensibility features and a drag and drop UI (for the most part anyway). Two sweet technologies, probably shouldn’t be compared to each other but we’re doing it anyway, pitted head to head against a data mapping task to the death (or at least to the recycling of my test VMs)… Now FIGHT!

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Recently I have been involved in researching and building a low-latency high-data-volume OLAP environment for a social entity and interaction analysis platform, the perfect mixture of concepts such as Big Data collection and processing,  large-scale Network Analysis, Natural Language Processing (NLP) and a highly scaled-out OLAP environment for end users to explore and discover data (essentially a Self-Service and Exploratory BI layer).

It is by all means not an easy mission to orchestrate all the technologies that back those concepts, particularly if you are interested in using the optimum solution for the problem at hand, for example Big Data might be better handled by a Hadoop layer, but Hadoop or Hive (at least on their own) are not geared up to respond to OLAP queries, which are real-time by nature, and even if they were, your end-user needs familiar tools and interfaces to analyse and study this data, which is where SQL Server Analysis Service and the whole Microsoft BI stack might come in and offer great integration with already existing business applications (such as Office or SharePoint).

This post discusses a few architectural approaches to exposing a Hadoop layer through a SQL Server Analysis Service (SSAS) interface, with references to data-latency, redundancy and over-all performance.

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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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