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Infographic: 4 Observability Anti-Practices

Our new infographic highlights four common observability anti-patterns.

Franz Knupfer

Published:

May 14, 2024

2 minute read

Table of Contents

In a recent blog we asked the question, “If data is the new oil, why is so much of it treated like old garbage?” It’s not hyperbole, at least when it comes to observability. For many enterprises, the huge volume of incoming log data has gotten so expensive that it’s typical to discard most or all of the underlying raw data through practices like sampling, aggregation, and short retention windows.

Even when enterprises keep the data, it’s often moved to tiered, less accessible storage, where it quickly goes dark. It can be rehydrated for compliance purposes or use cases like forensic analysis, but for the most part, it has minimal value—and data that’s left in the dark is still very expensive, comprising 52% of a typical enterprise’s storage costs. 

But we’ve already written a lot of words on this topic, so now it’s time for an infographic. If a picture is worth a thousand words, how valuable is an infographic, which combines pictures and words?

The following infographic discusses four observability anti-practices that either limit access to your data or lead to permanent data loss: short retention windows, sampling data, aggregating data, and limiting dimensionality. Read more about observability anti-practices.

So what should you do if it’s too expensive to retain all your data with your current observability solution?

Hydrolix is a streaming data lake designed for log-intensive use cases. If you’re ingesting at least a terabyte of log data every day, then Hydrolix can help you reduce your observability data costs—typically by 4x over incumbent platforms—while also allowing you to keep all your data “hot” and readily accessible for analysis even at petabyte scale.

Hydrolix achieves this cost efficiency by maximizing the strengths of commodity S3-compatible object storage—so you get real time streaming ingest and sub second query efficiency even on trillion-row datasets. It doesn’t matter whether your data is a minute or a year old—it’s all hot and ready to query.

Next Steps

Read Transforming the Economics of Log Management to learn about the issues facing many of today’s observability platforms, and how next-generation cloud data platforms must maximize the benefits of object storage to make log data cost-effective.

Learn more about how Hydrolix offers cost-effective data at terabyte scale and contact us for a POC.

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In a recent blog we asked the question, “If data is the new oil, why is so much of it treated like old garbage?” It’s not hyperbole, at least when it comes to observability. For many enterprises, the huge volume of incoming log data has gotten so expensive that it’s typical to discard most or all of the underlying raw data through practices like sampling, aggregation, and short retention windows.

Even when enterprises keep the data, it’s often moved to tiered, less accessible storage, where it quickly goes dark. It can be rehydrated for compliance purposes or use cases like forensic analysis, but for the most part, it has minimal value—and data that’s left in the dark is still very expensive, comprising 52% of a typical enterprise’s storage costs. 

But we’ve already written a lot of words on this topic, so now it’s time for an infographic. If a picture is worth a thousand words, how valuable is an infographic, which combines pictures and words?

The following infographic discusses four observability anti-practices that either limit access to your data or lead to permanent data loss: short retention windows, sampling data, aggregating data, and limiting dimensionality. Read more about observability anti-practices.

So what should you do if it’s too expensive to retain all your data with your current observability solution?

Hydrolix is a streaming data lake designed for log-intensive use cases. If you’re ingesting at least a terabyte of log data every day, then Hydrolix can help you reduce your observability data costs—typically by 4x over incumbent platforms—while also allowing you to keep all your data “hot” and readily accessible for analysis even at petabyte scale.

Hydrolix achieves this cost efficiency by maximizing the strengths of commodity S3-compatible object storage—so you get real time streaming ingest and sub second query efficiency even on trillion-row datasets. It doesn’t matter whether your data is a minute or a year old—it’s all hot and ready to query.

Next Steps

Read Transforming the Economics of Log Management to learn about the issues facing many of today’s observability platforms, and how next-generation cloud data platforms must maximize the benefits of object storage to make log data cost-effective.

Learn more about how Hydrolix offers cost-effective data at terabyte scale and contact us for a POC.