Whether applications or raw information, businesses are requiring faster access to varied data sets.
To a certain degree, hardware is an enabler in this regard. However, database software and data warehousing applications are an integral component of profiting from big data endeavors. Microsoft’s SQL Server 2014 has been lauded as a viable solution for organizations that want to conduct a number of tasks, such as:
- Efficiently scrutinize data
- Improve the processing power of multiple virtual machines
- Replicate data for disaster recovery/business continuity strategies
- Enhance Power BI’s back-end capabilities while improving its usability
The relevant database administrator
Although relational databases have been criticized for their inability to store unstructured information, engineers have been synchronizing them with data storage architectures such as Hadoop. It’s this type of cohesion, as well as other functions, that are making SQL training much more attractive.
SQL Server Pro Jayleen Heft asserted that SQL Server 2014 and its subsidiary technologies are transforming the skill sets of DBAs, who are “poised to play a key role in leading organizations into the future.” Not only is SQL Server 2014 compatible with Hadoop, it possesses NoSQL functionality such as complied stored protocols and in-memory applications.
Learning how to employ these SQL Server 2014’s ancillary tools requires thorough, instructor-led education that comprehensively breaks down the database. Specifically, what will DBAs learn from such courses?
According to the Microsoft Developer Network, users can now leverage clustered columnstore indexes to better compress digital information and deduce how well data warehouse read-heavy workloads are being conducted. This particular function can be updated frequently, meaning the CCI can carry out numerous insert, update and delete processes.
In addition, columnstore indexes now have SHOWPLAN and archival data compression. While the latter component allows one to further compress specific partitions of a columnstore index, the latter allows DBAs to presents information regarding columnstore indexes. For example, if you wanted to find values for ActualExecutionMode, you would either receive Batch or Row as your values.
In-Memory online transaction processing engine
Formerly known as Hekaton, SQL Server 2014’s In-Memory OLTP function enables users to migrate certain tables and store procedures into memory, SQL Server Pro contributor Michael Otey outlined. Otey maintained this decreases input/output traffic and drastically enhances companies’ OLTP software.
Arguably, SQL Server’s In-Memory OLTP tool is the most reputed feature the solution has to offer. Otey acknowledged Microsoft’s assertion that some programs may perform 20 times better than they did before running off SQL Server 2014. One particular company that provides product data to supplier retailers and search engines, recognized a 67-fold improvement from using in-memory OLTP.
What’s under the hood? Otey wrote that In-Memory OLTP eliminates locking delays by leveraging a concurrency control mechanism. This works by copying the In-Memory OLTP tables into memory and having a lock-free algorithm process transactions for tables residing in the memory.
Knowledge of these and other functions is becoming more applicable as the years progress. As more diverse data types continue to be produced by a variety of devices, services and applications, DBAs will be expected to use in-memory processing and other tools to the best of their abilities.
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