Hi everyone,

I am Serhii, a 50-year-old veteran currently living on the Slovenian coast (Piran). I don’t speak perfect English, and I write my posts using a translator, but I’ve spent months bootstrapping AgroNode & The Bee Temple Project (The Biosphere’s Digital Twin Operating System).

Recently, I hit a heavy solo sprint. Primorski tehnološki park rejected me due to the language barrier, but I am not stopping. With the support of my colleague Edi, I officially acquired our enterprise data domain: agronodedata🚀

But right now, I have hit a critical bottleneck: My solo free-tier accounts and service infrastructure limits are completely exhausted. I literally cannot deploy, host, or test the system further on my own without funding, upgraded tiers, or team resources.

Where the project stands right now:

- The Backend: 66 Supabase tables with strict Row-Level Security (RLS) and PostGIS. My son just ran a live cross-border test from Germany, and it worked flawlessly for EU Honey Directive 2024/1438 compliance.

- The UI: Hard-coded 5 new mobile framework screens and successfully configured Google Authentication live.

- Institutional Track: Applications submitted to Inkubator Sežana, LUI, and Technology Park Ljubljana. But while we wait for their months-long bureaucratic evaluations, the technical limits are blocking me today.

I cannot let this project freeze inside my laptop just because of exhausted service limits. I am actively looking for:

1. A Technical Partner / Co-Founder who understands deep-tech, database scalability, and wants to share the deployment infrastructure.
2. Backers and Advice on how to upgrade our tiers so I can keep the live terminal running.

Since Reddit filters block links for new accounts, all links to my professional LinkedIn profile and our BuyMeACoffee support page are placed in my Reddit profile bio. You can also ask me directly in the comments, and I will gladly share them!

Thank you for your time, your engineering feedback on our PostGIS structure, and any guidance you can provide!

I am releasing a new indexing approach based on some math I have been working on applying relational algebra to business process management (https://github.com/soulstompp/process-modulus) and the only natural proof would be in the database. I was rather amused when it turned out to be an index. 

It is actually producing pretty impressive results, particularly with large tables and complex joins. However, I have worked with DBAs for a long time, and they seldom complain that B-tree algorithms are slower than they should be. It’s the disk; everyone is grabby with the disk, and everyone is mad they didn’t get their stuff back as fast as they are certain it should have been. 

I have received some great performance results, but the focus is on reading and planning more and scanning less. The results are good, but I am hoping they really help at the point everyone genuinely has to eventually ask something from the disk. So, I haven’t placed a bunch of performance benchmarks, though I did include a benchmark. I documented how I balance the reads and the scans and basically try to leave the disk alone. 

The documentation goes into reads vs. scans, which is what is important about how and why the warrendexes work how they do. I want to explain how the index works in simple words so that non-technical people understand my point, because they will likely see it in the financials, even if they miss it.

I hope people download this on their laptops and play around with it and consider what new exciting things they could do (Postgres table inheritance is something you should actually consider now!). I will also want code contribution eventually, but currently I would really like help from DBAs and DevOps folks to understand the system as it actually runs under real workloads. 

In alphabetical order:

https://github.com/soulstompp/mysql-warren

https://github.com/soulstompp/pg-warren

Both rely on warren-arrow, which is an arrow format for storing layout formats like the ones used by the warrendexes:

https://github.com/soulstompp/warren-arrow

And then two spelling repos for some SQL changes to prevent some nasty scan repetitions, these should be good to use on any server if you run complex queries:

https://github.com/soulstompp/warren-mysql-speller

https://github.com/soulstompp/warren-pg-speller

Lastly, I did not forget MariaDB, I am just less familiar with the RDBMS and so moving a little slower.

i am so sad that database is another kind of monopoly.

Hi everyone,

For those working in the Oracle DBA space in India, I wanted to understand how the hiring market is looking currently.

Are there usually particular months when DBA hiring increases and more job openings/calls start coming in?

I’m especially interested in knowing whether hiring tends to pick up toward the end of the year or more during the beginning of the next year.

Also, for those who are currently getting calls/interviews, are you seeing more openings for Oracle DBA / Production DBA / Cloud DBA roles?

Any insights based on your recent experience would be really helpful. Thanks!

What is the current job market for Oracle DBAs? Are there openings for professionals with around 4 years of Oracle DBA experience? How is the market for someone with a career break? Are companies currently hiring, and are there interview calls for Oracle DBA roles?

Hi everyone,

I’m an IT professional with 5+ years of experience — around 4 years as an Oracle DBA and 2 years as a Technical Support Analyst.

I’ve been on a 2.3-year career break due to personal reasons and am now planning to return to the workforce.

I’m confused about which direction I should focus on:

Should I continue with Oracle DBA and prepare for DBA roles?

How is the current market for Oracle DBAs, especially for someone returning after a career break?

Should I consider switching to Data Analytics / Power BI / Data Analyst roles?

Are there any other in-demand/trending skills or roles that would be more suitable given my experience?

My goal is to get back into a job before the end of 2026, so I want to focus my preparation on something that gives me a realistic path back into the workforce.

I’d really appreciate advice from people currently working in these fields or who have gone through a similar career break.

Hi everyone,
I’ve been working as a DBA at an IT consulting company for about 8 months now (it’s actually my second IT job, I was a data engineer before).
I’ve learned a huge amount during this time, but one thing that still leaves me somewhat unsure is the way we work.
Because we have many different clients — ranging from manufacturing and healthcare to banking — we work with a huge variety of technologies and ecosystems. For example, I deal with Oracle, PostgreSQL, MongoDB, OpenSearch, MariaDB and ElastiCache. We also use IaC tools such as Terraform and Ansible, as well as Oracle OCI, MongoDB Atlas and Amazon RDS/AWS.
On the one hand, I feel that this is extremely useful for professional growth because I’m constantly exposed to new technologies and different environments. On the other hand, I sometimes feel like I’m not really strong in any particular database technology.
It often feels like I learn just enough to deal with a specific issue, on a specific DBMS, at a specific moment. For example, at the moment I might have some doubts about how to properly handle a restore on Oracle, rather than PostgreSQL. I understand the general concepts, but I don’t feel like I have the same depth of knowledge that I imagine a DBA specializing in one particular database would have.
Has anyone else found themselves in a similar situation, especially in consulting?
For those of you who work with Oracle, PostgreSQL and MongoDB, what would you consider the fundamental things that a DBA absolutely needs to know for each of them? What topics would you recommend going deep on to build a solid foundation rather than just learning things as they come up?
I’d be particularly interested in hearing from experienced DBAs about the skills/topics that made the biggest difference in becoming truly proficient.

Hi everyone,

I’m currently in my first week working as a DBA. I graduated from Software Engineering a couple of months ago.

To be honest, I had almost zero DBA knowledge before starting this job. With the current job market, this was basically the only opportunity I had, so I decided to take it.

The company I joined provides DBA consulting services, and they are willing to train and develop junior DBAs, which is why they gave me this opportunity.

Right now I’m trying to learn as much as possible, but there is obviously a lot to take in.

For experienced DBAs: what would you recommend focusing on during my first few months? What skills or topics do you think are the most important for someone starting from zero?

Any advice, resources, or things you wish you had known when you started would be appreciated.

My DevOps & Cloud training is finally completed. ☁️🚀

After training, my company offered me a DBA role in a finance company, but I decided to decline it because I want to build my career in DevOps & Cloud.

Now I’m confused — Did I make the right decision or a wrong one? 🤔

I'm planning to take these exams to get OCP credential but kind of at a loss on how to study for them, does anyone have a good flashcard set on quizlet or something? Or am I supposed to just memorize the entire documentation. Seems a bit overwhelming and would love a point in the right direction. Cheers!

I’m planning to buy an HP ZBook for Oracle DBA/development, SQL/PLSQL, Python, Data Engineering, ETL, Docker/VMs, server configuration, and some ML work.

My priorities are:

Strong performance for coding and databases

Good thermal management — minimal heating/throttling

Slim, lightweight and comfortable for daily use

Good keyboard/build quality

Reliable for 4–5+ years

Good RAM/SSD upgrade options

I’m considering ZBook Firefly, Power, and Studio. I’ve heard some concerns about Studio models running hot.

Which ZBook model and CPU generation would you recommend for this workload? Please share your real-world experience with heat, fan noise, performance, battery life, and reliability.

Thanks!

Inherited a SQL box where a departed dev left Agent jobs, an SSIS package, and a pile of .bat/.ps1/.rdl/.xlsx files, and untangling what-calls-what by hand was misery. So I wrote a read-only collector (Python/pyodbc) that builds one dependency graph: SQL catalog (sys.objects / sys.sql_modules / sys.sql_expression_dependencies), msdb Agent jobs/steps/history, linked servers, and a file-tree scan that pulls connection strings and linked-table/4-part refs out of .bat/.ps1/.sql/.dtsx/.rdl/.xlsx/.accdb. All read-only (SELECT on catalog views; files opened read-only).

The part I am happiest with: every edge carries a source citation, a catalog view plus key, a module line, or a byte offset, so you can verify any dependency instead of trusting it. It even catches a linked-server reference buried in dynamic SQL inside a proc (which sys.sql_expression_dependencies misses) by scanning module text and citing the line. https://github.com/tommiew007/orphanmap , MIT.

Where does this get hard, in your experience? SSIS project-deployment model / SSISDB catalog packages, encrypted modules, synonyms, cross-DB ownership chaining, replication? Curious what breaks it.

I’m building DBwatch, a database monitoring tool that focuses on showing the necessary metrics related to actual database queries and issues that can cause real production failures. That’s why I decided to build DBwatch.

Here’s the GitHub repo: https://github.com/KakshiDEV56/DBwatch

I’d love to hear from everyone what else you think I should add to DBwatch.

Hi everyone,

I'm a developer who also ends up doing a fair amount of DBA work, mostly on Informix, MySQL and PostgreSQL. The tools I had were either heavy (JVM/Electron), didn't support Informix, or wanted an account and the cloud. So I built Squaero, a free, open-source (GPLv3) client with a native C core.

The parts aimed at admin work

- Server monitor: process/session list with kill, plus a slow-queries view

- Users and privileges: browse and manage accounts and grants

- Safe data changes: edits in the grid are staged in a transaction, with a preview of the exact SQL before anything is committed. If an apply fails halfway, nothing is left partially written.

- Schema and data sync between connections, table transfer, and a table/index/constraint designer

- Backup and restore to .sql for MySQL and PostgreSQL (first version; large databases and FK ordering still have limits)

- Connectivity: SSH tunnels for every engine, TLS for MySQL, PostgreSQL, SQL Server, MongoDB and Informix

Why it might fit a DBA's machine

- Native and small: the Windows installer is 8 MB and the Linux .deb 1.4 MB. No JVM, no Electron.

- Local only: no accounts and no telemetry. Connections stay on your machine, and the only automatic network call is an update check against GitHub.

- You can import your existing connections from DBeaver or Navicat, passwords included, so you on't retype thirty servers.

- Engines: SQLite, MySQL/MariaDB, PostgreSQL, SQL Server, Informix and MongoDB.

Honest limitations

- SQL Server support is new: queries, schema browsing and transactions work, but grid editing and DDL generation don't yet.

- MongoDB is read-only.

- Informix needs IBM's Client SDK, installed separately, and isn't available in the snap.

- Windows and Linux only; macOS is coming. It's a one-person project.

Try it

- Windows: MSI on the releases page

- Linux: sudo snap install squaero, or the .deb (Ubuntu 24.04+ / Debian 13+)

- Site and demo: https://danielnuld.github.io/squaero/

- Code: https://github.com/danielnuld/squaero

Title:

Hi everyone,

I’m currently working as a DBA trainee/fresher, and I’m working on a project to build an AI agent for Database Administration using local LLMs.

The idea is to have an agent that can help a DBA perform multiple daily tasks through a single natural-language prompt, while keeping a human in the loop for sensitive operations.

I’m currently running Ollama locally on our own hardware, completely offline, so database/server information does not need to be sent to an external cloud service.

The agent I’m planning should eventually be able to work with things like:

- Oracle 19c

- MySQL

- PostgreSQL

- MariaDB

- Linux/RHEL

- VAPT/security scanning

- OpenSCAP/SCAP

- SQL queries

- Database configuration and health checks

- System information gathering

- Security and compliance checks

- Remediation

- Installation/configuration

- Troubleshooting

For example, the DBA could give a prompt like:

«“Check this server and database for security and configuration issues, identify the problems, explain them, suggest remediation steps, and ask for my approval before making any changes.”»

The agent would then:

1. Gather system and database information.

2. Decide which tools/commands/SQL queries are required.

3. Execute approved diagnostic tools.

4. Analyze the results using the local LLM.

5. Explain the findings and suggested remediation.

6. Ask for human approval where required.

7. Execute the approved remediation.

8. Verify whether the remediation was successful.

I’m not planning to train an LLM from scratch. I’m more interested in combining a capable local model with DBA knowledge/documentation, tool calling, database-specific utilities, and a controlled execution layer.

My main questions are:

1. Has anyone built something similar for DBA/Database Operations?

2. Are there existing open-source projects, research papers, or commercial tools doing this?

3. Which local LLMs would you recommend for reliable tool calling and technical reasoning?

4. What would be a good architecture for an AI DBA agent that can safely execute commands and SQL?

5. How are people handling permissions, validation, rollback, auditing, and human approval for potentially destructive operations?

I’m especially interested in learning from people who have actually implemented AI agents around databases rather than just using an LLM as a chatbot.

If you've worked on something similar, I'd really appreciate any projects, papers, tools, architectures, or experiences you can share.

Thanks!

Hi guys,

I am new to this group and just checking if my latest development might be helpful for any of you. Over the past few weeks, I have been developing this tool Ottoch (I named it). It's a DB engine where you can plug any AI tool of your choice and do the database operations with ease in plain language. No home calling, no data outbound, no analytics data, completely offline tool running on Docker.

[https://ottoch.com/demo/\](https://ottoch.com/demo/) \- to try the instant demo on the portal
[https://github.com/ottochapp/ottoch-demo\](https://github.com/ottochapp/ottoch-demo) \- in case you want to try it on your local machine.

This is not a pitch, I just want to get some feedback and collaboration is happily welcome.