Time for Your Observability Data Diet

New Year, New Data

Ah, January—the month of resolutions, gym sign-ups, and kale smoothies that nobody asked for. While everyone’s busy trying to shed those holiday pounds, let me ask you this:

When was the last time your observability data fit into its jeans?

That’s right. Your observability data lake—the one you were so excited to build—is starting to look more like an observability data ocean. Bloated, slow, and expensive. And guess what? It’s costing you more than your post-holiday therapy sessions.

But fear not! Just as humans have Ozempic for their New Year goals, I’ve got the playbook for trimming your data lake’s waistline while keeping it healthy, fast, and observant. So grab your running shoes (or just stay in your chair—it’s fine), and let’s talk about putting your observability on a diet.

Why Your Observability Data Needs to Slim Down

You start with good intentions

“We’ll log everything! It’ll be fine!”

and then six months later, your AWS bill could fund a small nation.

You’ve got metrics, traces, and logs from every microservice, pod, and hamster wheel in your system.

But here’s the thing: more data isn’t always better.

Those old-school CPU, memory, and disk metrics? They’re about as useful as an ab crunch machine at the gym. Observability 2.0 has moved on to wide events, raw data, and correlations that actually mean something.

If your data lake is choking on noise and redundant metrics, you’re not observing—you’re hoarding. And the worst part? All that extra weight is slowing you down when you need real-time insights.

The Observability Data Diet Plan

Time to Marie Kondo your observability stack.

Here’s how you can cut the bloat and save some cash without sacrificing performance:

1. Retention Policies: Keep Only What You Need

Ask yourself: Do you really need to keep logs from a failed deployment six months ago? Probably not. Implement smart retention policies.

• Actionable Tip: Start by setting shorter default retention windows (e.g., 30 days). Use tiered policies for critical vs. non-critical data.

2. Cheap, Deep Storage: S3 and Friends

Move infrequently accessed data to cheaper object storage like S3. It’s like swapping Wagyu beef for Costco chicken—still good, just way cheaper.

• Actionable Tip: Leverage S3’s intelligent tiering or Glacier for logs that are “nice to have” but rarely accessed.

3. Compression: The Ozempic for Your Data

Did you know some compression tools can shrink your data up to 170x? That’s like going from a double XL to a medium overnight.

• Actionable Tip: Look into modern compression algorithms like Parquet, Zstd or CLP. Bonus points if your observability platform supports them natively.

4. Aggregate Smarter, Not Harder

Instead of storing raw logs from every system forever, focus on high-value aggregates that give you the insights you need without the noise.

• Actionable Tip: Use tools like Prometheus for metrics aggregation, Vector.dev for your data pipeline or pre-processed logs to summarize key patterns.

5. Instrument for Value, Not Vanity

Do you need every HTTP 200 logged? (Spoiler: You don’t.) Instrumentation should focus on anomalies and trends, not just raw volume.

• Actionable Tip: Audit your telemetry. Remove useless metrics and ensure your instrumentation is aligned with actual business goals.

Words of Encouragement (and Humor)

Here’s the good news: your observability data lake doesn’t have to be a bottomless pit of despair. With a few tweaks, you can turn it into a lean, mean, insight-generating machine.

Think of it this way: Your data doesn’t need to hit the gym—it just needs better portion control. Keep what’s meaningful, compress what you can, and let go of the rest.

The goal isn’t just to save money (though you will). It’s about making your systems faster, more reliable, and more responsive when things go wrong.

So go forth, tech leaders, and embrace the data diet. Your budget (and your future self) will thank you.

Oh, and one last thing: If anyone asks, this article sparked joy, right?

What’s your observability resolution this year? Drop your thoughts below—I promise to reply before your S3 Glacier retrieval finishes.