Real-time streaming analytics
In today’s digital world, big data has a huge impact on everyone’s life. Not only it is used by professionals, but it also has a great influence in our personal lives, seemingly controlling everything around us. If you use a smartphone or social media, big data determines a great deal of what you watch, listen to, and read. Big data permeates all aspects of modern life; from health services to traffic. It is everywhere, which is why capabilities like streaming analytics are imperative for making sense of all the information.
Companies have started using cloud platforms and advanced data processing solutions to derive essential business insights. These insights enhance operational processes, improve customer service, and provide executives with critical data points. This market shift is driving the push to create more value from big data and investments in real-time analytics. The shift is also growing the need to use data science and machine learning for greater insight.
What can streaming analytics do for you and your business?
IT departments have been bombarded with requests for real-time analytics capabilities for years, even though some people who ask for the capabilities may not actually need them or even know what they mean. Is the high cost of moving to real-time analytics justified? Although the prices of memory, storage, and bandwidth all continue to fall, there are technology integration issues, process issues, and cultural issues to be considered. Adding to the confusion, there is no standardized definition of “real time.” Depending on who you ask, “real time” can be measured in anything from sub-seconds to a span of more than 24 hours.
Whether you work in stock trading, wealth management, Cloud infrastructure, SCADA systems, real-time bidding systems, marketing analytics, or social gaming, every industry has a market exposure to real-time.
Use streaming analytics to make informed decisions while events are happening
With real-time analytics, you can capture, integrate, analyze, and report the data in real-time as it comes in. Real-time analytics let you understand what’s working and what’s not working for you at that very moment. Since the information provided by real-time analytics is based on current data, it helps organizations identify process loopholes that impact the organization negatively. These real-time insights help identify, intercept, mitigate, and resolve errors immediately. The inability to resolve these errors can lead to disastrous operational failures and customer dissatisfaction.
Sharpe Engineering chooses IBM Streams as its stream processing platform
Jim Sharpe, the founder of Sharpe Engineering, found out that many of his clients across different industries were facing challenges in processing huge volumes of fast data in order to enable instant insights and act on the information. Sharpe has seen many software products that claimed to be able to meet this challenge of faster data analysis, but most of them quickly fell by the wayside. He read an article about a new IBM technology called System S that evolved into a solution we know as IBM Streams, and he built a strong working relationship with the development team.
Sharpe Engineering has built a successful business by using IBM Streams to help its clients solve a wide range of problems. Sharpe lists three major reasons for choosing IBM Streams over other providers:
Computational efficiency: According to the Linear Road Benchmarking study by Walmart in 2016, IBM Streams performed 20 times better than Apache Storm and twice as well as Apache Apex.
Maturity and stability: IBM Streams provides a highly reliable platform for mission critical applications.
Richness of tools: IBM Streams provides development tools for Java and Python and a host of sophisticated libraries for machine learning.
Learn more about how Sharpe Engineering leverages IBM Streams to help its clients solve problems like predicting post-traumatic epilepsy before patients develop it to detecting faint signals in underwater acoustics that have not previously been observed. Impossible? Not anymore.