Log management makes it easy to obtain the exact log file for analysis software to process or a developer to review.
To stay afloat among a sea of competitors, companies have had to evolve their strategies and increase their growth. That meant finding ways to grow faster while preventing, minimizing, and solving issues as soon as they occur. But as digital use of applications started growing from the millions to the billions, businesses and dev teams found themselves with terabytes upon terabytes of data that they need to process and analyze before they become irrelevant. Increasingly, many companies are turning to log management to make the best use of their growing volumes of data.
Big data is a term that describes massive amounts of data regarding a specific subject from one or a limited number of sources. Analyzing and processing big data allows companies to make better-informed decisions that help them in their business operations, but most notably in software development, debugging, and engineering. The information extracted from big data can help businesses improve customer service and experience, boost sales, minimize losses, and improve overall efficiency.
See also: Customer Experience Improvements Require Data Context
The problem with processing big data
The problem with big data is its size and rapid growth rate, as it usually includes the latest data on the subject matter. For big data to deliver on its promises and have a noticeable return on the investment of collecting and storing it, the processing needs to accommodate and take full advantage of the five pillars of big data. The five V’s of big data are volume, velocity, variety, veracity, and value.
Not processing big data correctly is a sure way to waste considerable time and resources on inaccurate or no longer relevant information. Big data is only useful for a small window of time before it becomes outdated, forcing you to start the analysis over.
Log management as an essential role in big data processing
Log management is the process of overseeing and managing log files and involves the processing of big data that can run into billions of log lines. Log management software organizes the log files in a set order to meet specific search criteria, making it easy to obtain the exact log file for analysis software to process or a developer to review.
Debugging process
Debugging is a never-ending process in the life of any application or software, as there’s always something to fix or improve to ensure continuous improvement. When testing for bugs or subpar performance, it’s vital for the devs to have accurate logs on hand that they can use to identify the exact reason for the malfunction.
Doing so frequently enough generates useful data of its own, adding to the bigger picture of the DevOps and allowing the analysis to predict where bugs are most likely to occur, the reason behind them, and how to fix them.
Security incidents and regulations
Managing security and its set regulations means the need to analyze the data coming in from any entrance or weak points. However, the process becomes more complicated—and near impossible—as the number of devices and endpoints in a network increases that turns security-related log files into data-rich databases that qualify as big data, which could require days or weeks to analyze manually. Log management provides security professionals with a real-time overview of the system’s security and sends out an alert of issues requiring immediate attention.
The proper analysis of such critical data is essential to the evolution of a network’s security model. For one, cybersecurity software, such as EDR, becomes more adept at predicting cyber threats by identifying attack patterns and recognizing harmful and suspicious behavior. In addition, knowing what caused the security incident and precisely what it entailed allows your business or team to provide concrete proof of following specific security regulations and guidelines, which are sometimes mandatory in terms of cyber-insurance.
Monitoring systems’ health
Similarly, as networks grow, so does the number of hardware and software they use, which need constant monitoring and quick intervention to ensure an acceptable level of performance. Log management stays on top of the data and log files and tracks hardware and software performance in real-time, sending alerts whenever one is under-performing. But when data processing and analysis really comes into play, it can provide ways to optimize software and hardware performance auditing and limit their downtime by setting regular reminders of when a piece of equipment needs maintenance or replacement.
DevOps environments
Creating DevOps environments is already a complex task, primarily due to the large number of people often involved in the process coming from various departments and with different technical backgrounds. Instead of ending up with uncontrollable data accumulated during the build process, log management keeps it organized and properly tagged. That makes it ready for processing and analysis whenever needed, without having to manually go through the entirety of a specific log file.
Performing regular or real-time log analysis ensures the health and performance of software, application, or DevOps environments. It allows for easy diagnosis of stacked data to efficiently solve issues—and prevent them from recurring—that would have otherwise taken considerable time and effort.
A/B and multivariate testing
Making well-informed decisions requires analyzing the outcomes of various options to see, which yields the best result. One way to do so is A/B testing. Often used in software and web development, a single A/B test can produce tons of data, depending on the sample size. The more users participate in the test, the more accurate the results. And data of that size needs proper logging and tagging to produce informative analysis.
Log management becomes more vital to the process when using multivariate testing. An A/B test can only look at two variables at a time, and running more tests can cost you time. At its core, multivariate testing is the same as running multiple A/B tests simultaneously, which results in more complex data sets to analyze. Instead of giving a yes or no answer to whether a feature works, multivariate testing measures the degree of effectiveness of each added feature and design element and how they work together to achieve the final goal.
The future of big data
In today’s competitive market, businesses and dev teams can’t afford to make a single wrong decision. Processing big data properly is essential to positive, evidence-driven business decisions. The average database volume is only expected to grow in the upcoming years and require new, more powerful tech to manage. The possibilities of big data are endless, even more so as machine learning, cloud technology, and artificial intelligence make it easier to work with and more accessible for businesses and dev teams on the smaller end.
