Amazon’s native monitoring and management service AWS CloudWatch is great for basic monitoring and alerts. However, on its own, it may not be the best solution for analyzing log data at scale — especially if you need to analyze data outside of AWS. Many teams may find themselves restricted by retention issues and basic analytic features with Amazon CloudWatch logs for troubleshooting use cases.
Whether cloud infrastructure logs across AWS services, applications, microservices, container logs, Lambda functions, security telemetry data, or network device logs, CloudWatch can be tough to use under the weight of non-stop log streams. Since cloud-based applications and infrastructure generate millions (even billions) of logs – teams might be losing out on a wealth of insights if they have to make retention tradeoffs.
Let’s dive into why log analytics are important, some of the challenges with the CloudWatch log process, and how to overcome them.
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If you’re analyzing logs in CloudWatch, chances are you might be looking for answers for ITOps, DevOps, security or customer analytics purposes. Let’s elaborate on each use case:
These use cases have a common problem: processing data at scale.
This issue crops up across a wide variety of observability, monitoring and dashboarding tools such as Elasticsearch, CloudWatch and Datadog. Fortunately, organizations can supplement these tools with alternatives that allow for powerful log analytics at scale with unlimited data retention.
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To execute properly on these use cases, organizations must be able to access large volumes and wide varieties of log data for analysis. One of the biggest challenges with CloudWatch is that it can quickly become expensive and requires care in how it is deployed and utilized at scale. Furthermore, while CloudWatch is great for creating alarms and monitoring real-time application performance – it’s not ideal for deeper troubleshooting use cases.
When it comes to troubleshooting and root cause analysis, CloudWatch has a complex UI. Once organizations collect a high enough volume of logs, filtering and searching in the CloudWatch interface becomes far too complicated. Finding the root cause of an error involves scrolling manually through pages and pages of CloudWatch log groups to locate the specific invocation that threw an error.
Even then, data integration might still be a problem. CloudWatch lacks the data integration depth and correlation features necessary to recognize very complex patterns or perform root cause analysis across larger and multiple data sources.
In addition, querying and scaling data isn’t the best use case for CloudWatch. Once teams reach terabyte-scale (and need log retention beyond a short period of time, such as a few days or a week), CloudWatch simply becomes impractical. This is especially true if you need a longer retention period for compliance reasons or to tap into the value of long-term log storage for the use cases described above.
READ: Achieving Better CloudWatch Log Insights
New cloud-based platforms can alleviate some of the most common bottlenecks with log data:
“They rapidly and dramatically compress indexed data, which is critical given the high number and small content of logs. Users can automatically discover, normalize, and catalog all those log files and assemble metadata to improve query planning—all with a smaller footprint than predecessors such as Lucene. The log data remains in place, but presents a single logical view with familiar visualization tools the user already knows (such as Kibana via open APIs).”
- Kevin Petrie, VP of Research, Eckerson Group
To search data or run complex queries, organizations need to extend their log analytics strategy with other tools like ChaosSearch that support sophisticated log queries and parsing multiple logs at once. Getting started is easy:
Using ChaosSearch, organizations can take advantage of unlimited data retention with greater price transparency at scale. Combining CloudWatch with ChaosSearch can help organizations achieve best-in-class monitoring, as well as a deeper understanding of all of the systems that drive business growth.
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