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he speed of traditional query processing (or lack thereof!) has always been a problem for smaller enterprises. Just because you’re a small business, it doesn’t mean you have small data needs. Thankfully, the tech world has recognized that data needs are changing. And the good news is that now you can access incredibly fast query processing, no matter what size your business is. The solution? SQL Server BI and its in-memory analytics capabilities.

New SQL BI from Microsoft can turn this…

Into this…

Previous BI tools did not allow users to run real-time transactional and analytics workloads at the same time on the same system. It was only possible with an extra analytics system for reporting. This attempt to increase processing speed came at an immensely heavy processing (and financial) burden that most smaller businesses simply couldn’t afford. SQL Server 2016 has changed all that.

Real-Time Operational Analytics
The challenges of being forced to run 2 separate systems for operational and analytical workloads affect some crucial aspects if you’re looking increase your query processing speed. Using ETL requires complex code to extract the data you want. It also comes a high price, considering the cost of additional hardware and software. ETL also affects data latency. Some business cannot accept non-real-time data analysis because it is a part of the business’s core operations (e.g. fraud detection in online financial transactions).

Real-time operational analytics is a powerful weapon in the SQL BI arsenal. It uses columnstore indexes to run analytics queries directly on the operational/transactional workload. Previously, this would slow down the operational workload a great deal but SQL Server 2016 seems to have a double-pronged remedy: filtered columnstore indexes and analytics query offloading to readable secondary.

Super-charge your SQL

So, how does SQL Server 2016 help you as a small business?

Irrespective of the size of your business, a complex data model can burden your system very quickly. SQL Server 2016 uses in-memory analytics to improve the performance of transaction processing, data ingestion and data load. It has been optimized for stored procedures and transient data scenarios. Because your databases are processed in-memory, your data gets retrieved and processed much faster, also thanks to SQL Server’s support for multi-threaded architecture. The best part is that even if your data becomes more complex, it will hardly make any difference to query processing speed.

SQL Server’s business intelligence tools can handle the addition of analytics and reporting with no effect of processing speed. These tasks can run within the in-memory database as stored procedures with no problem. The results speak for themselves. In addition to exceptionally fast performance improvements, you can also expect true real-time analytics and a less complex programming model. Most importantly, you don’t need to have a separate OLAP system because everything already gets processed within the database. Even if your business has advanced analytic needs, like supervised machine learning or predictive processes, SQL Server’s BI capabilities do not bat an eyelid. In fact, it is much improved because of these in-memory optimizations.

SQL Server 2016 is 47% Faster than 2014!

Now, you’re probably thinking that in-memory processing makes a system run faster but it means buying more (expensive) memory, right? Well, time doesn’t really equal money in this instance. Compressing your data will enable you to store more in your existing memory. When calculating how much memory you need, use the size of the compressed data and the results may surprise you. Also, remember that not all data will be pulled into memory. SQL Server will only use data that is necessary, thus making a fast database even faster. If your query processing speeds need a boost (and it does, otherwise you wouldn’t still be reading this), SQL Server 2016 has you covered, no matter how big or small your processing needs are.

Want to see exactly how different SQL licenses stack up? Click HERE to find out!

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