Logs are a living transcript of your infrastructure, sites, and applications. They record every click, request, error, and anomaly. This adds up to millions of entries pouring in from many sources. How do you keep from drowning in all that data, while still getting insights?
That’s where log management comes in. Let’s break it down.
What Is Log Management?
Log management is the practice of collecting, storing, and analyzing log data from across your entire digital ecosystem: your applications, databases, infrastructure, network, and more.
A strong log management solution helps you:
- Improve app/site performance
- Improve security
- Improve IT implementation
- Uncover business insights
The Log Management Lifecycle
Here’s a quick overview of how modern log management typically works. The steps include:
- Creation
- Streaming and forwarding
- Processing
- Storage
- Analysis
- Deletion and archiving
1. Log Creation
Almost all services emit logs: servers, applications, cloud services, databases, firewalls, CDNs, and more. External services often come with built-in log creation features, while engineering teams and technical practitioners need to build logging for their homegrown services. In the case of homegrown solutions, teams often need to think carefully about which attributes will be captured and how they should be formatted.
2. Log Streaming and Forwarding
Logs must be collected from various disparate sources, and to ensure they are available for real-time (or near-real-time) analytics, many solutions use streaming ingestion. For high-volume logging environments, you need fast streaming and adequate buffering to avoid losing data.
3. Log Processing
Log data can be structured or unstructured. While unstructured data provides additional flexibility and can sometimes be faster to store (because no transformation process occurs before storage), there are a number of downsides to leaving log data unstructured, including:
- Slower query times (and higher compute costs) because data isn’t optimized before storage.
- Harder to interpret and understand results—and to create queries that accurately return the data you’re looking for.
- Lost context, such as information about where the logs came from or when they arrived.
As a result, it’s typically a best practice to use an ETL (extract—transform—load) process to first standardize, enrich, and optimize log data before it is stored. Modern logging solutions can use streaming ETL processes to transform log data quickly so it’s still available for analysis in near real time.
4. Log Storage
Logs are stored, either in a centralized location (such as with traditional log management) or in multiple locations (such as with data federation). The type of storage used can have significant impacts on cost, performance, and data retention, especially with larger log volumes. Solid state drives (SSDs) and in-memory solutions can retrieve data for analysis very quickly, but can be prohibitively expensive. In contrast, object storage is cost-effective and infinitely scalable, but needs to be optimized for performance and won’t achieve real-time millisecond latency. Fast time to glass and near real time analytics are still possible with the right solution, making object storage a better choice for the majority of high-volume use cases.
5. Monitoring and Analysis
Logs are the foundation for application monitoring, observability, and many other use cases such as data science and cybersecurity. To get meaningful insights from their data, teams often use dashboards, visualizations, alerting, and queries. This requires querying the underlying data, and possibly using a federated approach if the data is distributed across many sources. These queries may occur on incoming live data, such as for operational use cases, or the data may be much older, such as in the case of forensic analysis, data science, and training machine learning models.
6. Deletion and Archiving
Logs are often tagged for deletion or archiving after a specified period of time. As the volume of data continues to grow, so too does the pressure on deleting and archiving data. Archiving often means moving data to cold or frozen storage, which typically needs to be rehydrated (a time-consuming process), as opposed to hot storage, where data remains available for querying.
Many enterprises discard operational logs after a short period of time (without archiving) as log volumes grow because the cost to store them can be prohibitive with their existing solutions.
Log Sources in Modern Ecosystems
In modern digital ecosystems, logs can come from a wide range of infrastructure and application sources, including:
- Network logs: These include logs from servers (such as access and error logs), routers, proxies, CDNs, and WAFs.
- System logs: These include kernel logs and systems-level operations logs.
- Security logs: These can include logs from SIEM (security information and event management) solutions, EDR (endpoint detection and response) solutions, and other security platforms. But many other types of logs can be considered security logs as well—for example, authentication logs are important for security and can be emitted by many services, such as networks, servers, and firewalls.
- Application logs: These are logs generated by your applications and custom middleware.
- Database logs: These provide information on data usage, such as queries that were executed, which users accessed the database, and error and performance issues.
- Infrastructure logs: In addition to network logs, infrastructure logs can include logging from cloud services such as AWS and container orchestration services such as Kubernetes.
- Sensor and mobile logs: These can include logs from remote devices, such as sensors and mobile phones.
There are various ways one might categorize all the different types of logs. No matter what they’re called, the important part is thinking through the logs you’ll want to collect and store. For example, you may have containers or other cloud-based, ephemeral entities that often scale up and then quickly disappear. You may realize you need to forward those logs to ensure that data isn’t lost. Regardless of the source or use case, if log data isn’t collected in the first place, you’ll have gaps in your data that you’ll be unable to recover or backfill.
Streaming Logs in Real Time
Once logs are generated, they need to be sent, often in real time, to a location where they can be processed and stored. This process requires tools that can stream high volumes of data without bottlenecks.
Buffering plays a key role here. If your logs surge during traffic spikes or attack events, can your system keep up? Streaming solutions must be fast, resilient, and scalable.
Another factor is log transformation. This is where decisions around ETL vs. ELT architectures come into play: whether you transform logs before or after storage can affect how quickly you can stream and store that data.
For a small application that stores logs locally, this process is very simple. But large distributed systems collecting logs from around the globe often take additional steps to ensure performant streaming. For example, data can be stored in edge servers or in multiple data centers. And cost-effective object storage is also distributed in nature, requiring enterprises to take advantage of techniques like high throughput and parallelism to maximize their performance.
Log Transforming and Processing
Logs should typically be processed and structured before they are stored. Here are some common transformation steps that occur:
- Parsing breaks logs into structured fields. For example, a log line may be a single string with multiple attributes (such as timestamp, event type, and so on). Breaking these into separate fields makes it easier to query and compress data.
- Enrichment adds context to logs through various metadata, like tagging logs with their associated server, app, or cloud region—or any other attribute that might help with later querying.
- Normalization ensures consistency across logs from different sources. This makes it easier to correlate and analyze logs from disparate data sources more easily.
- Compression helps with storage. Some approaches significantly compress log data while still enabling fast queries.
Interested in learning more about transforming log data? See this list of data transformation best practices.
Different configurations may process logs at different stages—when they are streamed, after ingestion, or even at query time. Some rarely-used logs may not be processed at all and may be stored in their original unstructured format until they’re needed for analysis.
Learn more about ETL (extract—transform—load) and ELT (extract—load—transform) in ETL vs ELT: A Detailed Guide.
Monitoring and Analyzing Data
The real value of logs lies in your ability to monitor and analyze them. This is the main reason why enterprises ingest and retain log data in the first place (beyond compliance).
Whether you’re hunting for threats, debugging an outage, or looking for long-term patterns, your ability to search and analyze logs is essential for detecting and mitigating issues quickly. Visualization tools and dashboards such as Grafana and Superset can help bring trends into focus, while alerting systems can notify you of anomalies and other issues.
Many stakeholders and teams rely on log analysis, including operations, security, and data science practitioners. For example, security teams increasingly rely on logs not just for catching obvious exploits but for identifying slow, stealthy threats and risks of data leaks and exfiltration. And as long as data is retained long-term, these insights can be available for years after incidents occur.
Data Deletion and Archiving
The final stage in the log management lifecycle is archiving and eventually data deletion—though some log data is simply discarded without archiving. There are a number of valid reasons when data should be deleted, such as due to regulatory and compliance requirements.
However, archiving and deletion are often driven by cost pressure. At high data volumes, the cost of retaining data can be extremely expensive, especially with premium solutions for use cases like observability and cybersecurity. To manage those costs, data is either quickly moved into frozen storage where it can be difficult to rehydrate or it’s simply discarded altogether. Fortunately, modern log storage solutions that use object storage can allow enterprises to retain their data far longer at much lower costs—all while keeping it accessible for fast queries.
Log Management Benefits
Let’s take a quick look at some of the benefits of log management.
Security and compliance: Logs are your first line of defense — and your last line of evidence. They help detect intrusions, investigate data breaches, and maintain regulatory compliance. Many companies such as Splunk use SIEM (Security Information and Event Management) systems, and many data sources ranging from flow logs to CDN logs contain valuable security insights and information about breaches and malicious attempts.
Application performance: Logs help you debug and trace performance issues. Whether you’re trying to fix a slow-loading page or diagnose a backend error, logs can tell you what went wrong, when, and why. Long-term log storage also helps identify application performance trends over time.
IT operations: For IT teams, logs help track hardware rollouts, system updates, and internal tool performance. Over time, this can reveal deployment and maintenance issues that impact efficiency.
Business intelligence: Logs can provide deep insights on the state of your business, allowing you to make decisions that impact the bottom line—whether that’s optimizing inventory, rightsizing infrastructure, or detecting fraud.
Better machine learning models: Machine learning models often benefit from large volumes of data, and log data provides a constant incoming source of data about everything from your applications to your users which you can use for prediction, anomaly detection, and more.
The Biggest Challenge: The Increasing Scale of Log Data
With systems becoming more complex and interconnected, and bandwidth needs always rising, the volume of log data is skyrocketing. Data is being collected at such high rates that we’ve had to invent new words to measure it (like zettabyte). A 2024 survey by Dell found that 43% of IT decision-makers fear their data infrastructure won’t be able to handle future data demands.
This creates various challenges, including:
- A risk of “drowning in data”: It can be hard to extract practical insights from such a huge amount of data.
- Overwhelming storage costs: It can be expensive to store huge amounts of data, especially for longer time frames.
- More security risks: Many log sources and storage repos can create several security challenges, like a risk of storing sensitive data or, and a risk of discarding important data.
These factors make it especially important to have a strong log management strategy that considers your logging needs well into the future.
Planning Your Log Management Strategy
Before you dive into tools and tech stacks, start by working through some core questions:
- What are all your different types of logs? Are they structured? Ephemeral? Mission-critical?
- What’s the total log volume you have? How might that grow over time?
- What are you trying to achieve with your logs? (Security? Performance? BI? All of the above?)
- For your different goals, do you need both real-time analysis and the ability to quickly query historical data?
- Based on those goals, how long do you need to keep your various types of logs?
- To meet your goals, what kind of speed of access/querying does your team need?
- What kind of budget do you have to work with?
You want solutions that let you make use of your data without big bills — especially as log volumes continue to grow.
