I’m Kayla, and I run blue team work at a mid-size fintech. I’ve lived with both a security data lake and a SIEM. Same house. Same pager. Very different vibes. For a deeper dive on how the two square off, check my hands-on comparison.
Here’s the thing: both helped me catch bad stuff. But they shine in different ways. I learned that the hard way—at 2 a.m., with cold pizza, on a Sunday.
Quick setup of my stack
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SIEMs I’ve used: Splunk and Microsoft Sentinel. I also tried Elastic for a smaller shop.
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Data lakes I’ve used: S3 + Athena, Snowflake, and Databricks. I’ve also set up AWS Security Lake with OCSF schema (learn more about OCSF here).
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Logs I feed: Okta, Microsoft 365, CrowdStrike, Palo Alto firewalls, DNS, CloudTrail, VPC Flow Logs, EDR, and some app logs.
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If you want a vendor-by-vendor breakdown, read my candid review of six data lake platforms.
We ingest about 1.2 TB a day. Not huge, not tiny. Big enough to feel the bill.
Story time: the quick catch vs the long hunt
The fast alert (SIEM win)
One Friday, Sentinel pinged me. “Impossible travel” on an exec’s account. It used Defender plus Okta sign-in logs. KQL kicked out a clean alert with context and a map. Our playbook blocked the session, forced a reset, and opened a ticket. It took 20 minutes from ping to fix. Coffee still hot. That’s what a SIEM does well—fast, clear, now.
The slow burn (data lake win)
A month later, we chased odd DNS beacons. Super low and slow. No one big spike. Over nine months of DNS and NetFlow, the pattern popped. In Snowflake, I ran simple SQL with our threat list. We stitched it with EDR process trees from CrowdStrike. Found patient zero on a dev box. The SIEM had aged out that data. The data lake kept it. That saved us. (I outlined the build-out details in this big-data lake story.)
So what’s the real difference?
Industry write-ups such as the SentinelOne piece on Security Data Lake vs SIEM: What’s the Difference? echo many of these same themes and complement the hands-on lessons below.
Where a SIEM shines
- Real-time or close to it. Think seconds to minutes.
- Built-in rules. I love using KQL in Sentinel and SPL in Splunk.
- Nice playbooks. SOAR flows work. Button, click, done.
- Great for on-call and triage. The UI is friendly for analysts.
My example: I have a KQL rule for OAuth consent grants. When a new app asks for mailbox read, I get a ping. It tags the user, the IP, and the risky grant. I can block it from the alert. That saves hours.
Where a security data lake shines
- Cheap long-term storage. Months or years. Bring all the logs.
- Heavy hunts. Big joins. Weird math. It’s good for that.
- Open formats. We use Parquet, OCSF, and simple SQL.
- Freedom to build. Not pretty at first, but flexible.
My example: we built a small job in Databricks to flag rare service account use at odd hours. It scored the count by weekday and hour. Not fancy ML. Just smart stats. It found a staging script that ran from a new host. That was our clue.
The messy middle: getting data in
SIEMs have connectors. Okta, Microsoft 365, AWS CloudTrail—click, set a key, done. Normalized fields help a lot. You feel safe.
Data lakes need pipes. Our stack had Glue jobs and Lambda to push logs to S3. We mapped to OCSF. Once, a vendor changed a field name in the Palo Alto logs. The job broke at 3 a.m. I learned to set schema checks and dead-letter queues. Boring, but it keeps the night quiet. If you’ve ever watched your pristine lake turn into a swamp, my week of chaos story breaks down that slippery slope.
Cost, in plain words
- SIEM cost grows with GB per day. Splunk hit us hard when we added DNS. Sentinel was kinder, but high too.
- Data lake storage is cheap. Compute can spike. We used auto-suspend in Snowflake and cluster downscaling in Databricks.
- Our blend: high-signal logs to the SIEM (auth, EDR, firewall alerts). Everything else to the lake. That cut our SIEM bill by about 40%, and we still kept what we needed.
Tip: set hot, warm, and cold tiers. We keep 30 to 60 days hot in the SIEM. The rest goes cold in the lake. I know, simple. It works.
Speed and lag
SIEM: near real-time. Feels like a chat app for alerts.
Data lake: minutes to hours. AWS Security Lake was usually 1–5 minutes for us. Big batch jobs took longer. For hunts, that’s fine. For live attacks? Not fine.
People and skills
Analysts love SIEM UI. It’s clear and fast. Our juniors fly there.
Engineers love the lake. They tune ETL, write jobs, and build views. SQL, Python, and a bit of KQL know-how helped the whole team meet in the middle.
We wrote simple how-tos: “Find risky OAuth grants” in KQL, then the same hunt in SQL. It eased the gap.
For teams that need an even friendlier bridge between heavy SQL and point-and-click SIEM dashboards, a service like Basenow lets you spin up quick, shareable queries against both data sources without waiting on engineering. I also dissect how a data hub compares to a lake in this hands-on piece.
What I run today (and why)
I use a hybrid model.
- SIEM for alerts, triage, and SOAR. Think: Okta, EDR, email, endpoint, firewall alerts.
- Data lake for long-term logs, hunts, and weird joins. Think: DNS, NetFlow, CloudTrail, app logs.
A small glue layer checks rules in the lake every 5 minutes and sends high score hits to the SIEM. It’s a tiny alert engine with SNS and webhooks. Not pretty. Very handy.
Real hiccups I hit
- Sentinel analytic rules were great, but noisy at first. We tuned with watchlists and device tags.
- Splunk search heads slowed during big hunts. We had to push the hunt to Snowflake.
- Glue jobs broke on schema drift. We fixed it with schema registry and versioned parsers.
- OCSF helped a lot, but we still kept some raw fields. Mappings aren’t magic.
You know what? The pain was worth it. I sleep better now.
Side note: when the night shift drags on, my team boosts morale by trading cybersecurity-flavored jokes and memes—everything from phishing gags to tongue-in-cheek takes on risky texting habits. If you need a quick laugh break (and a reminder of how quickly messages can go off the rails), check out these curated sexting memes that round up the funniest and cringiest examples of sexts gone wrong, serving equal parts comic relief and cautionary tales about digital privacy.
In that same spirit of examining public content for security lessons, exploring real-world classified listings provides a hands-on way to practice spotting social-engineering tricks and privacy pitfalls. A handy sandbox is the local postings on Backpage Smyrna where you can review ad structures, metadata, and common scam patterns—perfect raw material for building or testing parsers and detection rules before they ever touch production data.
Quick chooser guide
Still weighing a classic warehouse? Here’s my side-by-side take on lakes vs warehouses.
Use a SIEM if:
- You need fast alerts and ready playbooks.
- You have a smaller team or newer analysts.
- Your data size is modest, or you can filter.
Use a security data lake if:
- You keep lots of logs for months or years.
- You do big hunts or fraud work.
- You want open formats and cheaper storage.
Best result, in my view: do both, with a plan.
Tips that saved me
- Pick a common schema early (OCSF worked for us).
- Tag your crown jewels