I’ve built both. A clean data lake that felt like a tidy pantry. And a messy data swamp that felt like a junk drawer… with water. I wish I was kidding.
(If you enjoy “from-the-trenches” war stories, you’ll like this related read on a week that swung from order to disorder: Data Lake vs. Data Swamp: My Week From Calm to Chaos.)
I’m Kayla. I work with data for real teams. I spend my days pulling numbers, fixing pipelines, and—yes—naming files better than “final_v3_really_final.csv.” You know what? Names matter.
Here’s my very real take: what worked, what broke, and how it felt.
First, plain talk
- Data lake: a big, safe place to store all kinds of data. It’s organized. You can find stuff. It’s easy to reuse.
- Data swamp: same “big place,” but messy. No clear labels. Old junk. You can’t trust it. It smells funny, in a data way.
Sounds simple. But it isn’t, once people start rushing.
My calm place: the lake I set up on AWS
I built a lake on S3 for a retail team. We used Glue for the Data Catalog. We used Athena to query. We stored files as Parquet. We partitioned by date and store_id. It wasn’t fancy. It was steady.
(For another hands-on story about standing up a lake for massive datasets, check out “I Built a Data Lake for Big Data—Here’s My Honest Take”.)
A real path looked like this:
s3://company-analytics/sales/p_date=2025-10-01/store_id=042/
We kept a clear table name: retail.sales_daily. Columns were clean. No weird types. No mystery nulls.
I ran this query to check refund rate by store for October. It finished in about 12 seconds and cost under a dollar.
SELECT store_id,
SUM(refunds_amount) / NULLIF(SUM(gross_sales),0) AS refund_rate
FROM retail.sales_daily
WHERE p_date BETWEEN DATE '2025-10-01' AND DATE '2025-10-31'
GROUP BY store_id
ORDER BY refund_rate DESC;
We tagged fields with PII labels in Lake Formation. Email and phone had row and column rules. Marketing saw hashed emails. Finance saw full data, with a reason. I could sleep fine at night.
We also set a rule: one source of truth per metric. “Net sales” lived in one model. If someone tried to make “net_sales2,” I asked why. Sometimes I sounded bossy. But it saved us later.
Pros I felt:
- Fast, cheap queries (Parquet + partitions help a lot)
- One catalog everyone used
- Easier audits; less Slack noise at 2 a.m.
- Data trust went up; meetings got shorter
Cons I hit:
- Setup took time
- Permissions were tricky for a week
- People wanted shortcuts; I had to say no
My chaos story: the swamp I inherited
At a past job, I walked into an old Hadoop cluster. HDFS folders held years of CSVs from everywhere. No schema. No docs. File names like sales_2019_final_fix.csv and sales_2019_final_fix_v2.csv. You could feel the pain.
Two real moments still bug me:
-
A Q2 sales report went bad. The “qty” and “price” columns swapped in one feed for one week. Only one week! We didn’t notice for days. The chart looked great, but our units were wrong. My stomach dropped when I found it.
-
PII showed up in a “scratch” folder. Customer emails sat in a temp file for months. Someone copied it to a shared drive as a “backup.” Not great. I had to file a report and clean up fast.
Daily work took longer. A request like “What’s churn by region?” would take two hours. Not because the math is hard, but because I didn’t trust the inputs. I’d sample rows. I’d trace the source. I’d hope it wasn’t the “v3” file.
Pros (yes, there were a few):
- Quick to dump new data
- Anyone could add files
Cons that hurt:
- No catalog; only hallway knowledge
- Duplicate tables, odd column names, broken types
- Costs rose because queries scanned junk
- Big risk with privacy and legal rules
A simple test: can you answer this in 5 minutes?
“Show me weekly active users for last week, by app version.”
- In my lake: I had a clean table users.events with a date partition and a documented app_version field. Five minutes, one query, done.
- In the swamp: Three folders had “events.” One had JSON inside a CSV (yep). I spent 30 minutes just picking a table. The number changed by 12% based on the file I used. Which one should I trust? That’s the whole problem.
Swamp signs (if you see these, you’re there)
- Files with names like final_final_v9.csv
- Same column with three names (user_id, uid, userId)
- No data dictionary or catalog
- Email or SSN in temp or “scratch” folders
- People paste CSVs in chat to “prove” their number
Just like keeping your closet from turning into a heap of forgotten outfits, sometimes you want a quick solution that doesn’t leave long-term clutter. For instance, you might rent a statement dress for a single event through One Night Affair—their curated collection lets you shine for an evening and return the gown afterward, illustrating how a “use-it-once, return-it-clean” mindset keeps closets tidy in the same way naming conventions and governance keep a data lake from devolving into a swamp.
Letting anyone toss unchecked files into a shared bucket feels a lot like the anything-goes atmosphere on local classified boards: listings pop up fast, quality varies wildly, and there’s no real gatekeeper. A quick browse of Backpage Lorain on One Night Affair shows exactly how chaotic an uncurated marketplace can become; exploring that page gives you a vivid, real-world parallel for why ungoverned data drops quickly morph into a swamp that’s impossible to search or trust.
Peer-reviewed research keeps confirming what practitioners feel in their gut: without governance, lakes rot. One recent longitudinal study that tracked schema drift across dozens of enterprise repositories highlights exactly how quickly a “lake” can regress once naming conventions slip (arXiv:2312.13427).
How we pulled a swamp back to a lake
This was not instant. But it worked. Here’s what actually helped:
- We picked one storage format: Parquet. No more random CSVs for core tables.
- We used a catalog (Glue). Every table got a description and owner.
- We added table tests with Great Expectations. Simple checks: no nulls in keys; values in range.
(If you’re evaluating ways to keep bad data out of your lake, see “I Tried a Data Lake Testing Strategy—Here’s My Honest Take”.) - We set folders by topic: sales/, product/, users/. Not by person.
- We used dbt for models and docs. Each model had the source listed and a short note.
- We set retention rules. Old junk got archived.
- We masked PII by default. Only a few folks saw raw.
One more tip: we ran a “fix-it Friday” for four weeks. No new data. Only cleanup. We deleted 143 tables. It felt scary. It also felt like spring cleaning for the brain.
Tool notes from my hands
- AWS S3 + Glue + Athena: solid for a lake. Cheap, clear, and boring in a good way.
- Databricks with Delta tables: great for streaming and updates. Time travel saved me twice. If you’re evaluating a lakehouse route, the Databricks’ own data lake best practices guide is a solid checklist worth skimming.
- Snowflake: fast, great for shared data. The “zero-copy clone” was handy for tests.
- Airflow for jobs. Simple, loud alerts. I like loud.
- Great Expectations for tests. Start small. Even one “not null” test pays off.
(Still shopping around? Here’s a blunt review of “I Tried 6 Data Lake Vendors—Here’s My Honest Take”.)
For teams that don't want to assemble all these parts themselves, BaseNow packages data lake best practices—catalog, governance, and cost controls—into a managed service you can spin up in minutes.
None of these fix culture. But they make good habits easier.
The cost story no one wants to hear
A swamp looks cheap on day one. No setup. Just dump the files. But then you pay with time, risk, and stress. My Athena spend in the lake stayed steady because