INTRODUCTION

Logs are automatically-generated records of events that take place within an application, network, or infrastructure service.

Examples of events that generate log data include failed password attempts, changes in network configurations, clicks and other interactions within an app. These records are stored as log files, creating an audit trail of system events that can be analyzed for a variety of purposes. This log data is critical to discovering everything from security threats to application performance issues.

Many organizations store their log data for a set retention window, then archive this data in cold, long-term storage where it’s difficult to regularly access for analytics. In fact, some organizations might retain their logs for just a few days, depending on the minimum windows dictated by local or industry-specific compliance regulations.

Either that, or they’re overpaying for costly storage, search and analytics options. For example, using a combination of Elasticsearch, Logstash and Kibana (the ELK stack) can add up. With modern applications routinely generating terabytes of machine data per month, ELK stack costs can quickly spiral out of control due to the distributed architecture in which data is partitioned and stored (more on that later).

Many of these same organizations also rely on separate observability tools to interpret their log data for specific use cases, such as application performance and security threat hunting. While these tools are very good at alerting you about what’s happening in real time, they are not designed or optimized for analytics at scale. They typically limit data retention or become expensive for long-term retention. A wealth of insights from log data is likely being left on the table. What’s more, each individual observability platform can create a data silo, whereas many teams find much more value in centralizing different sets of log data.

First, let’s look at four common use cases for log analytics.

Luckily, there’s a better way to perform log analytics at scale, without paying outrageous storage costs.

We’ll show you how analyzing log data can help your organization

Comply

with internal policies and external regulations

Detect

and investigate security threats

Troubleshoot

systems, computers, applications or networks

And More!

First, let’s look at four common use cases for log analytics