The Rising Value of Big Data in Application Monitoring

In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.

From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis to find breakthroughs and marketable insights.

Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource. 

According to a recent study by Sigma Computing, the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.

Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021. 

In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.

Analysis & Structured Data

One of the basic premises of application monitoring is to derive detailed visibility in the performance and user experience of applications along with the supporting infrastructure and networks.

Hence, the primary objectives of application monitoring tools are to rapidly diagnose and resolve issues to help IT and DevOps deliver consistent application performance. 

By throwing Big Data in the mix, application monitoring tools will have an incredible range of information to collect, store, and analyze.

One of the basic premises of application monitoring is to derive detailed visibility in the performance and user experience of applications along with the supporting infrastructure and networks.

Hence, the primary objectives of application monitoring tools are to rapidly diagnose and resolve issues to help IT and DevOps deliver consistent application performance. 

By throwing Big Data in the mix, application monitoring tools will have an incredible range of information to collect, store, and analyze.

However, if we focus on only structured data rather than unstructured data, the process can be further stimulated. Unlike unstructured data, structured data is highly organized, factual, and to-the-point, resulting in more accurate analysis of data and much lesser time.

Data-Centric Approach to Security

Data-centric security is an approach to security that emphasizes the security of the data itself rather than the security of networks. Since enterprises have become increasingly reliant on digital information to run their business, big data projects have become mainstream. 

Therefore, to implicitly protect data, application monitoring can be implemented to allow the organization to overcome loopholes that present a viable threat of data being lost or abused by those with malicious intent.

This can lead to similar benefits to combining low code with AI for your business. Key concepts include:

• Discovering what data is stored, including sensitive information.
• Manage the ability to define access points, parameters, and policies for data to be accessed as well as blocked for specific users and locations.
• Protect against data loss or unauthorized use of data.
• Monitor data usage and identify meaningful deviations from normal behavior.
• Implement policies that consistently protect data as it moves in and out of applications and storage systems. 

Keep Scalability in Mind

It is important to note here that the world’s data is increasing progressively and at a rapid pace. This is why Big Data poses such a challenge for APM (application monitoring) in the first place.

This problem extends beyond simply chasing down hindrances for just day-to-day activities or present-day applications. With time we will observe an increase in the use of IoT (Internet of Things). 

When that happens, real-time detection of anomalies at a larger scale will become unavoidably significant. Hence regardless of whether APM challenges or IoT-related applications preoccupy your establishment, Big Data will inadvertently become essential, whether you like it or not.

Hence for any organization that seeks to maintain its operations and achieve self-efficacies, a scalable and efficient Big Data architecture or pipeline would become mandatory.

Modern APM Tools for New Thresholds

It is not like our tools aren’t usable. It is just that new tools always seem to push towards new thresholds and deliver better results.

We are currently living in a world where generating several terabytes of operations data within a day has become increasingly common. Simply looking at the cost of storing and amassing so much data is a cost-intensive struggle, plus that is only just one part of the actual problem. 

Even if you store that much data, the analysis of such huge chunks of information would inevitably suffer from its own shortfalls. For a true solution, you require APMs that deal with real-time behavior and accurately reading through large and complex archives.

Aspiring learners who seek essay writers‘ assistance understand just how much new tools and software are making a difference.  Some of the best AMP tools available in the market include:

1. Alertbot
2. Cisco
3. Datadog
4. Dotcom-Monitor
5. Dynatrace
6. New Relic
7. Stackify
8. Smartbear
9. SolarWinds
10. Uptime Robot

Optimize Performance

It is probably the best way to optimize application performance, or for troubleshooting, you require visualization for the entire data pipeline with the help of a single view.

Applications like DRIVEN can help you achieve that by drilling down details of each execution along with complete visualization of your entire data pipeline. Through visualization, you will be able to see where your application is spending most of its time. 

This will, in the end, help you to determine and address all the potential anomalies and bottlenecks that can hamper the performance of your application.

Moreover, by going through your application process’s performance history and statistics, you can compare key execution metrics and analyze trends to identify one-offs from reoccurring related conditions. Pupils who seek a reliable coursework writing service UK understand well how optimizing performance for application monitoring can help to handle Big Data.

Root Cause Analysis for Anomalies

One of the biggest questions that need to be answered is how do APM tools address a problem. While simply identifying anomalies can be looked upon as a step in the right direction, however, every anomaly has its own underlying issues. 

This is where root cause analysis becomes an obligation to ensure the stability of the system. The operations team always struggles when it comes to finding the root cause behind an irregularity within the system.

One of the biggest questions that need to be answered is how do APM tools address a problem. While simply identifying anomalies can be looked upon as a step in the right direction, however, every anomaly has its own underlying issues. 

This is where root cause analysis becomes an obligation to ensure the stability of the system. The operations team always struggle when it comes to finding the root cause behind an irregularity within the system.

However, with data bulging out of proportions and reaching incredible size, streaming several terabytes of data to conduct analysis in real-time environments can pose a considerable challenge. 

Therefore, to effectively uncover the root cause, you require analysis of historical data and the ability to perform real-time analysis without any indiscretions. In the end, AMP tools recognize time series data and the correlation of events with their respective time stamps. Once the sequences of an anomaly are traced back in time, accurate root-cause analysis can be performed.

Using Machine Learning to Your Advantage

With artificial intelligence and machine learning, making waves worldwide and industries, there is no doubt that using ML for Big Data seems like a natural intervention. The proposed idea is to use machine learning to continuously build self-improving models.

Here machine learning can help to pin down anomaly detection based on application behavior that deviates from the norm. 

Machine learning algorithms can prove to be useful for collecting, analyzing and integrating data for large organizations. ML can also be introduced for data labeling and segmentation, along with data analytics and scenario simulation.  

Conclusion

As an ending note, I would just like to say that Big Data’s future depends on powerful APM tools and the integration of AI and ML into fictional systems. Together with incredible computing power and deep learning capabilities, businesses can accomplish unprecedented feats by reaching incredible milestones and reducing the time and effort required to conduct analysis. 

Side by side, we also cannot negate the importance of protecting valuable data from falling into the hands of those with malicious intent. Let’s keep our fingers crossed and see what the future holds for us when it comes to Big Data and application monitoring. For now, this is it. Cheers and all the best for your future endeavors!