As the speed and complexity of business continue to accelerate, the key to achieving a competitive advantage is not just having data—but knowing what to do with it.
Modern businesses need to make increasingly sophisticated decisions, quickly, and those who can optimize their advanced analytics capabilities to generate fast, useful and actionable data-driven insights reap the rewards. A recent Forrester report has revealed that the so-called “insights-driven businesses” that have managed to achieve this advanced state of analytics competency are growing at an average of more than 30 percent annually and are on track to earn $1.8 trillion by 2021.1
The secret to enabling these better, faster decisions is to take a holistic approach to analytics acceleration. Shrinking time to insights across the enterprise requires optimization from hardware to software, throughout the solution stack.
Performance-optimized hardware and infrastructure
At the hardware and infrastructure level, the choice of processors, memory capacity, storage media, network technologies, and cluster architecture can greatly influence the speed and quality of your analytics workloads. To enable your business to manage, secure and rapidly harness intelligence from your data, it is important to create a comprehensive analytics strategy that addresses the distributed nature of data—from the data center to the edge of the network.
This strategy should establish the core infrastructure capabilities you will need to support a broad range of analytics workloads. Important decisions to be made range from identifying the right silicon processors at the heart of your system to choosing performance-optimized technologies that will minimize the latency of memory-intensive data applications.
Investing in building this strong foundation is the key to delivering the optimal performance, scalability, and responsiveness required by your advanced analytics software.
Even with the optimal hardware and infrastructure in place, advanced analytics applications can be optimized to deliver more effective results through the addition of acceleration libraries. Deploying software libraries can be the turning point in enabling advanced analytics projects such as machine learning or deep learning, as they facilitate accelerated math, compression, storage, and parallelization without requiring any other hardware or software changes.
At Intel, we are continually working to optimize popular tools, libraries and frameworks—such as BigDL* from Spark MLib* to TensorFlow*, Caffe*, Theano* and Torch*, among others—so that they are fully compatible with the most modern Intel® architecture platforms.
Industry-standard frameworks and operating systems
Accessing industry-standard tools, frameworks and applications enable analytics software developers to work smarter and faster reducing compatibility issues and the need to reinvent the wheel.
Analytics software platforms from industry-leading software vendors and open source software solutions are optimized to deliver amazing performance for analytics workloads, which makes it easier to meet unique analytics needs.
Fully-tuned solution stack blueprint
The ultimate way to accelerate analytics is to bring all of these considerations together by deploying a fully-tuned, integrated solution stack from a blueprint which enables you to create a consistent architecture across your on-premises and cloud environments. With a standardized solution stack in place, you no longer need to invest time, money and resources in evaluating which hardware and software integrations are the best combinations for your analytics workloads.
One such example is our Intel® Select Solutions for Microsoft SQL Server*, one of the world’s most popular relational database management systems (RDBMS).2 Designed specifically with SQL Server 2016 in mind, this solution stack, including both hardware and software components, has been tried and tested to deliver excellent performance reliability and agility on the platform. What’s more, these solutions also include future-ready technologies built to help reduce the complexity of evolving data center needs.
To find out more about how you can optimize the solution stack, read our solution brief, "Accelerate Insights with a Holistic Analytics Infrastructure".
Intel® technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at www.intel.com.
Intel® compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel® microarchitecture are reserved for Intel® microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
2 Austrian consulting company Solid IT conducts ongoing research into the relative popularity of global database management systems on its DB-Engines ranking: https://db-engines.com/en/ranking [last accessed March 2018]