I took a little break, but I'm excited to be back sharing with you all.
Here’s what I’ve been working on lately—can’t wait for your thoughts!
Check it out and let me know what you think!
Read here:
https://dub.sh/dkXim1l

I’m thrilled to join the AWS Community Builders 2026 cohort! 🎉☁️
My first official deep dive is live. "Postgres=tables, Mongo=docs" is a myth. At scale, it's really about MVCC write amplification vs. WiredTiger Copy-on-Write.
Read the internal trade-offs here: https://dub.sh/L60SfSH
#AWSCommunityBuilders #AWS #PostgreSQL #MongoDB #DatabaseArchitecture

I've spent a lot of time thinking about how tough it is to keep our data safe.
Each setup—on-prem, hybrid, or cloud—has its own security headaches, and it's not always clear which is the trickiest.
Each setup—on-prem, hybrid, or cloud—has its own security challenges.
Personally, I find hybrid setups trickiest because of juggling different policies and controls.
What’s your experience?

I always find myself choosing between a detailed HTML report and a quick terminal summary.
What do you prefer when checking your test results?
I really like seeing a clear summary of my test results, but sometimes a visual report helps me spot issues faster.
What works best for you? Let me know your thoughts!

I’ve tried a few different operating systems for managing databases, and each one has its own perks.
Curious—what do you use for your database projects?
I use MacOS for most of my database work because it feels stable and flexible.
What OS do you prefer for database management?
Let me know in the comments!

Enhancing update performance by avoiding index modification when the updated column isn't part of any index. This relies heavily on "Page Pruning" to clean up dead tuples on the fly without a full VACUUM.
This is the difference between a database that scales and one that chokes on write amplification.
Check out the book: https://dub.sh/RNV0WzR And join the upcoming event: https://dub.sh/S0Vsj3W
#Postgresql #Internals #HOTUpdates

MongoDB performance often gets oversimplified. We've all heard "just index it," but that ignores the nuance of execution stages.
I'm tuning 𝗶𝗾𝘁𝗼𝗼𝗹𝗸𝗶𝘁 to dig into the actual damage: pinpointing if a query is dying during the Scan, Sort, or Fetch phase.
The goal is a tool that acts as a second opinion:
- Identify the query shape.
- Highlight the bottleneck stage.
- Suggest the fix.
If you could automate one specific insight for MongoDB, what would it be?
#MongoDB #iqtoolkit #Database

IQToolkit Analyzer v0.3.0rc1 is out on TestPyPI. This RC focuses on contracts-first outputs, config validation, and CLI UX polish. Try it and share feedback.
Install (RC):
- pip: `pip install iqtoolkit-analyzer==0.3.0rc1`
- TestPyPI: `python -m pip install --index-url https://dub.sh/HapcWZL --extra-index-url https://dub.sh/iWuUJfr iqtoolkit-analyzer==0.3.0rc1`
Docs: https://dub.sh/lg5iPIc
Issues: https://dub.sh/q4gZuBf

Postgres hides the problem (silent locks). MongoDB buries it (millions of log lines). 💀
Debugging databases feels like detective work where the evidence is working against you.
We’re building 𝗶𝗾𝘁𝗼𝗼𝗹𝗸𝗶𝘁 to catch both the Postgres Ghost Lock and the Mongo Scan Storm automatically.
Stop grepping. Let the tool solve it.
#DevOps #MongoDB #Postgres #iqtoolkit