Accurate Log Analytics — for the greater good of devs
In today’s digital world, businesses generate enormous amounts of machine data from logs and events from different parts of the technology stack. This data can provide crucial analytics and insights into applications, infrastructure, and business performance. Businesses require better log analytics to make sense of the data, which can offer faster troubleshooting, proactive monitoring, and insightful data analysis and reporting. According to a MarketsandMarkets research study, the Log Management Market is anticipated to expand from USD 2.3 billion in 2021 to USD 4.1 billion by 2026 at a Compounded Annual Growth Rate (CAGR) of 11.9% from 2021 to 2026. In this blog, I will explain why accurate and intelligent log analytics is essential, the challenges of managing vast amounts of log data, and best practices for getting accurate log analytics.
Why is better log analytics needed?
Log analytics aims to ensure that the collection of log data is useful for the organization. It provides multiple benefits that assist organizations in maximizing application performance and enhancing cybersecurity. However, these advantages can only be obtained if log analytics are performed effectively and efficiently.
- Determine root cause faster on high cardinality logs: High cardinality logs refer to logs containing many unique values for a given field or attribute. For example, logs that track user activity on a website might have high cardinality for the user ID field. Logs from applications, infrastructure, and networking also often contain high cardinality data like unique IP addresses, session ids, and instance ids. They can be challenging to store, convert, and analyze as metrics because traditional analysis methods may not provide enough insight into the data. Accurate log analytics tools, on the other hand, can handle the complex and diverse data structures found in high cardinality logs and provide deeper insights into the data. These tools can help identify patterns, anomalies, and other vital information that might otherwise go unnoticed, by centrally collecting the data, sorting through it, and providing tools to make sense of it. Accurate log analytics can also improve operational efficiency by helping identify issues and errors in real time. This allows teams to quickly respond to problems and prevent them from escalating into more significant ones.
- Gather business insights from log data: Log analytics reduces the need for multiple tools by reducing data silos. The same log data can also be used for example to gain insights that are useful for business operations teams. It enables real-time monitoring of business operations to better understand business performance, including identifying patterns and tracking KPIs. With better visibility into operations, organizations can make more informed decisions that drive growth and profitability. Organizations can gain valuable insights into customer behavior and preferences by analyzing customer logs. This helps tailor products and services and meet customer needs, increasing customer satisfaction and loyalty.
- Proactive monitoring: Proactive monitoring is one of the critical benefits of log analytics. With better log analytics, IT teams can continuously monitor and view application performance, system behavior, and any unusual activity across the entire application stack. This provides the opportunity to eliminate issues before they affect performance. By leveraging best practices for log analytics, organizations can improve operational efficiency, reduce downtime, and enhance the overall user experience. Log analytics tools can be configured to send real-time alerts based on pre-defined thresholds in the log data. Log analytics can identify correlations between different events or data points, pinpointing the root cause of issues. By consolidating log data across other systems and applications into a single location, log analytics tools make it easier for IT teams to monitor and troubleshoot issues across the entire application stack.
- Troubleshooting: Troubleshooting is a systematic method of problem-solving. It is used first to detect and then rectify issues across systems. A sound log analytics system helps unify, aggregate, structure, and analyze log data to provide the opportunity for advanced troubleshooting. It gives you a baseline of all your log data as it is received, which lets you gain insight before setting up a single query. Event logs accurately describe what happened and provide relevant information like timestamps and error messages while automatically omitting other causes through a simple elimination system. With this level of insight, you can trace issues to their root cause while seeing how components interact to help identify correlations. You can also view the surrounding events that occurred just before or after a critical event and more effectively pinpoint the problem.
- Security/Compliance: The importance of log analysis in addressing security and compliance concerns cannot be overstated. One of the primary reasons why organizations should care about log analysis is conducting forensics during investigations to understand and respond to security incidents such as data breaches. In the event of a suspicious activity or security incident, logs can provide valuable information to help organizations understand what happened and how to prevent similar incidents from occurring. By analyzing logs, organizations can identify and determine the extent of the breach and take steps to prevent similar incidents from occurring. This information can also be used to support legal and regulatory compliance requirements.
For example, in 2019, Capital One suffered a massive data breach that affected over 100 million customers in the United States and Canada. The breach was caused by a misconfigured firewall in Capital One’s cloud infrastructure, which allowed an attacker to access customer data stored in an Amazon Web Services (AWS) S3 bucket. By analyzing log data from various sources, such as servers, applications, and network devices, the organization could identify the indicators of compromise and determine the extent of the breach.
Challenges of managing vast amounts of log data
Businesses face two main challenges when managing vast amounts of log data. One challenge is the state of the environments themselves because modern business environments are increasingly distributed. The second challenge is the data size challenge, where it feels like finding a needle in a haystack while ensuring you have enough relevant information. These both can get quite daunting. Let us break them down further to understand the challenges.
- Volume: To meet security standards, a company must thoroughly review all its logs using a log analyzer. However, this can be a difficult task, especially for large log data volumes, as it may burden IT resources. Traditional log monitoring tools especially may not be as efficient as desired.
- Variety in formats: Logs may come in different formats depending on their source, making it challenging to conduct log analytics. Most log monitoring solutions use a standard format, but not all logs can comply with it. So, even…Read More
