Always regarded as a non-critical part of day-to-day operations in the past, Big Data and its delayed analysis was relegated to batch processing tools and monthly meetings. Today, as the IT industry has snowballed into a fast moving avalanche of Cloud, virtualization, outsourcing and distributed computing, the science of extracting meaningful intelligent metrics from Big Data has become an important and real-time component of IT Operations.
Why Big Data in Cloud Performance Tools?
No longer do IT management systems work in vertical or horizontal isolation as just a few years ago. The inter-dependence between IT Services, applications, servers, cloud services and network infrastructure has a direct and measurable impact on Business Services.
The amount of data generated by these components is huge and the rate at which this data is generated is so fast that traditional tools cannot keep up with any kind of real time correlation. The combined volume of data generated by this hybrid infrastructure can be huge, but if it is correlated properly, it can give misson critical insight into:
- the response times and behavior of an IT service or application
- the cause of performance degradation of an IT service
- trend analysis and proactive capacity planning
- see if SLAs are being met for business services
This data has to be analyzed and processed in real-time in order to provide proactive responses and alerting for service degradation. The data that is being collected can be structured or unstructured, coming from a variety of systems which depend on each other to offer optimal performance, and has little to no obvious linkage or keys to one another (i.e. the data coming from an application is completely independent of the data coming from the network that it is running on).
Some examples of data sources that need to be correlated are application logs, netflow, JMX, XML, SNMP, WMI, security logs, packet analysis, business service response times, weather, news, etc.
Enterprises are moving to hybrid cloud environments at an alarming rate and all customer surveys indicate that the complexity of these platforms are their biggest concern. Enterprises must adopt monitoring systems that are flexible and can handle Big Data efficiently so that they can offer real-time responses to alarms and get meaningful business impact analysis from all of the different data sources.
Contextual analytics and presentation of data from multiple sources is invaluable to IT Operations in troubleshooting poor application performance and user satisfaction.
As a simple example, a user response time application could send an alert that the response time of an application is too high. Application Performance Monitoring (APM) data could indicate that a database is responding slowly to queries because the buffers are starved and the number of transactions is abnormally high. Integrating with network netflow or packet data would allow immediate drill down to isolate which client IP address is the source of the high number of queries.
How to Handle Big Data for Cloud Performance
Traditional monitoring or BI platforms are not designed to handle the volume and variety of data from this hybrid IT infrastructure. The management platforms need to be designed to correlate Big Data from the IT components in real-time and provide feedback to the operations team for proactive responses. As these monitoring systems evolve, their Big Data correlation components will become richer and more analytical and will position these enterprises for the IT environments of the future.
New generation enterprise monitoring solutions that are scalable, have predictive analytics, multi-tenant and a granular security model are now available from a small number of vendors. Single use systems that are designed for just network data or just application data are trapped within the same boundaries that makes Big Data meaningless - by its very nature, Big Data systems need to be able to handle a very wide variety of data sources to provide greater uptime from faster troubleshooting and lower OpEx from correlated analysis.
Vikas Aggarwal is CEO of Zyrion.
The Latest
Broad proliferation of cloud infrastructure combined with continued support for remote workers is driving increased complexity and visibility challenges for network operations teams, according to new research conducted by Dimensional Research and sponsored by Broadcom ...
New research from ServiceNow and ThoughtLab reveals that less than 30% of banks feel their transformation efforts are meeting evolving customer digital needs. Additionally, 52% say they must revamp their strategy to counter competition from outside the sector. Adapting to these challenges isn't just about staying competitive — it's about staying in business ...
Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey ...
SLOs have long been a staple for DevOps teams to monitor the health of their applications and infrastructure ... Now, as digital trends have shifted, more and more teams are looking to adapt this model for the mobile environment. This, however, is not without its challenges ...
Modernizing IT infrastructure has become essential for organizations striving to remain competitive. This modernization extends beyond merely upgrading hardware or software; it involves strategically leveraging new technologies like AI and cloud computing to enhance operational efficiency, increase data accessibility, and improve the end-user experience ...
AI sure grew fast in popularity, but are AI apps any good? ... If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment ...
If you were lucky, you found out about the massive CrowdStrike/Microsoft outage last July by reading about it over coffee. Those less fortunate were awoken hours earlier by frantic calls from work ... Whether you were directly affected or not, there's an important lesson: all organizations should be conducting in-depth reviews of testing and change management ...
In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...
On average, only 48% of digital initiatives enterprise-wide meet or exceed their business outcome targets according to Gartner's annual global survey of CIOs and technology executives ...
Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...
