Hi, I’m Kayla. I work in data, and I touch this stuff every day. I’ve set up lakes for retail, ads, and IoT. I’ve stayed up late when things broke. I’ve watched costs creep. And yes, I’ve spilled coffee at 2 a.m. while fixing a bad job.
If you want the full test-drive narrative across all six platforms, I’ve published it here: I tried 6 data lake vendors—here’s my honest take.
I used each tool below on real projects. I’ll share what clicked, what hurt, and what I’d do again.
AWS S3 + Lake Formation + Athena: Big, cheap, and a bit noisy
I ran our clickstream lake on S3. Around 50 TB. We used Glue Crawlers, Lake Formation for access, and Athena for SQL. Parquet files. Daily partitions.
- Real example: We pulled web events from Kinesis, wrote to S3, and let analysts query in Athena. On Black Friday, it held up. I was scared. It was fine.
What I liked
- Storage is low cost. My bill was close to what I expected.
- Tools everywhere. So many apps work with S3.
- Lake Formation let us set table and column rules. Finance got only what they needed.
What bugged me
- IAM rules got messy fast. One deny in the wrong spot, and nothing worked.
- Small files slowed us down. We had to compact files nightly.
- Athena was fast some days, slow others. Caches helped; still, it varied.
Tip: Partition by date and key. Use Parquet or Iceberg. And watch Athena bytes scanned, or you’ll pay more than you think.
For a deep dive into locking down access, the AWS docs on how Athena integrates with Lake Formation’s security model are gold: secure analytics with Lake Formation and Athena.
Azure Data Lake Storage Gen2 + Synapse: Polite and locked in (in a good way)
I used ADLS Gen2 for IoT data from 120k devices. We used Synapse serverless SQL to query Parquet. Access was set with Azure AD groups. It felt… tidy.
- Real example: We stored sensor data by device and date. Engineers used Synapse to trend errors by region. We used ACLs to keep PII safe.
What I liked
- Azure AD works clean with storage. Easy for our IT team.
- Folder ACLs made sense for us. Simple mental model.
- Synapse serverless ran fine for ad hoc.
What bugged me
- Listing tons of small files got slow. Batch writes are your friend.
- ACLs and POSIX bits got confusing at times. I took notes like a hawk.
- Synapse charges added up on wide scans.
Tip: Use larger Parquet files. Think 128 MB or more. And keep a naming plan for folders from day one.
Google Cloud BigLake + GCS + BigQuery: Smooth… until you leave the garden
I set up a marketing lake on GCS, with BigLake tables in BigQuery. We pointed SQL at Parquet in GCS. It felt simple, and that’s not a small thing.
- Real example: Ads and email events lived in GCS. Analysts hit it from BigQuery with row filters by team. The queries were snappy.
What I liked
- IAM felt clean. One place to manage access.
- BigQuery did smart stuff with partitions and filters.
- Materialized views saved money on common reports.
What bugged me
- Egress costs bit us when Spark jobs ran outside GCP.
- Scans can cost a lot if you don’t prune. One bad WHERE and, oof.
- Cross-project setup took care. Small, but real.
Tip: Use partitioned and clustered tables. Add date filters to every query. I know, it’s boring. Do it anyway.
Quick side quest: if you’re hunting for a quirky public dataset to practice location filters, scraping, and sentiment parsing, I once pulled a snapshot of Lakeland, FL classifieds from a modern Backpage clone—Backpage Lakeland—the listings are free to browse and deliver messy, semi-structured ad data (titles, prices, geopoints) that’s perfect for stress-testing partitioning strategies and text-cleaning pipelines.
Databricks Lakehouse (Delta): The builder’s toolbox
This one is my favorite for heavy ETL. I used Databricks for streaming, batch jobs, and ML features. Delta Lake fixed my small file pain.
- Real example: I built a returns model. Data from orders, support tickets, and web logs landed in Delta tables. Auto Loader handled schema drift. Time Travel saved my butt after a bad job.
What I liked
- Delta handles upserts and file compaction. Life gets easier.
- DLT pipelines helped us test and track data quality.
- Notebooks made hand-off simple. New hires learned fast.
What bugged me
- Job clusters took time to start. I stared at the spinner a lot.
- DBU costs were touchy. One long cluster burned cash.
- Vacuum rules need care. You can drop old versions by mistake.
Tip: Use cluster pools. Set table properties for auto-optimize. And tag every job, so you can explain your bill.
For a nuts-and-bolts walkthrough of how I assembled an enterprise-scale lake from scratch, see I built a data lake for big data—here’s my honest take.
Need an even richer checklist? Databricks curates a thorough set of pointers here: Delta Lake best practices.
Snowflake + External Tables: Easy SQL, careful footwork
We used Snowflake with external tables on S3 for audit trails. Finance loved the RBAC model. I loved how fast folks got value. But I did tune a lot.
- Real example: Logs lived in S3. We created external tables, then secure views. Auditors ran checks without touching raw buckets.
What I liked
- Simple user model. Roles, grants, done.
- Performance on curated data was great.
- Snowpipe worked well for fresh files.
What bugged me
- External tables needed metadata refreshes.
- Not as fast as native Snowflake tables.
- Warehouses left running can burn money. Set auto-suspend.
Tip: Land raw in S3, refine into Parquet with managed partitions, then expose with external tables or copy into native tables for speed.
Dremio + Apache Iceberg: Fast when tuned, quirky on Monday
I ran Dremio on top of Iceberg for ad-hoc work. Reflections (their caches) made some ugly queries fly. But I had to babysit memory.
- Real example: Product managers ran free-form questions on session data. We set row-level rules. Reflections hid the pain.
What I liked
- Iceberg tables felt modern. Schema changes were calm.
- Reflections gave us speed without lots of hand code.
- The UI made lineage clear enough for non-engineers.
What bugged me
- Memory tuning mattered more than I hoped.
- Early drivers gave me a few gray hairs.
- Migrations needed careful planning.
Tip: Keep Iceberg metadata clean. Compact often. And pick a strong catalog (Glue, Nessie, or Hive metastore) and stick with it.
Costs I actually saw (rough ballpark)
- S3 storage at 50 TB was near a little over a grand per month. Athena was up and down, based on scanned data.
- Databricks varied the most. When we cleaned up clusters and used pools, we cut about 30%.
- BigQuery stayed steady when we used partitions. One bad unfiltered scan doubled a week’s spend once. I still remember that day.
- Snowflake was calm with auto-suspend set to a few minutes. Without that, it ran hot.
If you’ve ever struggled to visualize how small, usage-based meters balloon into a scary invoice, it helps to study a simpler system that still charges by tiny “units.” The InstantChat team turns the concept into a quick game called Token Keno—after playing through their probability walkthrough and using their calculators, you’ll walk away with an intuitive feel for how millions of micro-charges (tokens, scanned bytes, etc.) add up so you can budget your lake more confidently.
Your numbers will differ. But the pattern holds: prune data, batch small files, and tag spend.
So… which would I choose?
- Startup or small team: S3 + Athena or BigQuery + GCS. Keep it simple. Ship fast.
- Heavy pipelines or ML: Databricks with Delta. It pays off in stable jobs.
- Microsoft shop: ADLS Gen2 + Synapse. Your IT team will thank you.
- Finance or audit first: Snowflake, maybe with external tables, then move hot data inside.
- Self-serve speed on Iceberg: Dremio, if you have folks who like tuning.
Honestly, most teams end up mixing. That’s okay. Pick a home base, then add what you need.
And if you’re weighing whether to stick with a lake or branch into data mesh or data fabric patterns, my side-by-side breakdown might help: [I tried data lake,