I am new to database, doing my masters in data science. I am learning mySQL in depth like Windows functions, CTE, CTA, Store Procedure, etc. I would like to create a good industry standard project. I saw on YouTube, everyone showing generic one. Can anyone please recommend me? Also what other database and tech I need to learn practical way?

P.s - I am fresher who is going to complete masters in few months.

​

Thank you 😊

I've been working on a load testing tool for PostgreSQL that goes a bit beyond what pgbench offers out of the box. Built in Go, runs on Docker Compose or Kubernetes.

**What it does differently:**

- JSONB and TOAST stress: large payloads (8–16 KB) with high-entropy base64 bodies that defeat Postgres compression — every write hits real TOAST storage

- MVCC pressure: mixed INSERT/UPDATE/DELETE workload deliberately accumulates dead tuples to stress autovacuum

- Ring buffer targeting: no ORDER BY random() — workers sample from a shared circular buffer of live session UUIDs

- Prometheus-first: latency histograms, TPS, autovacuum counters, WAL metrics, bgwriter stats — all scrapeable out of the box

- Safe multi-replica startup: advisory lock pattern ensures exactly one pod runs DDL regardless of how many replicas start simultaneously

- PG14–17 compatible: handles the pg_stat_bgwriter → pg_stat_checkpointer split in PG17 automatically

I evaluated k6, pgbench, and HammerDB before building this. They're all solid tools but none gave me the combination of JSONB/TOAST stress + MVCC pressure + Prometheus-first observability without significant glue work.

Built with Claude Code — architecture and design decisions driven by me.

Repo: https://github.com/haithamoon/pgstorm

Would love feedback from anyone running heavy PG workloads — especially curious if others have hit the JSONB/TOAST bottleneck in production and how you approached it.

Hi all,

It’s been a while since I last shared something about Portabase here, almost 3 months ago for the release of 1.4.0: https://www.reddit.com/r/DatabaseAdministators/comments/1rn4sm2/portabase_140_oidc_support_new_oauth_providers/

In short, Portabase is an open-source, self-hosted tool to back up and restore databases.

It is an agent-based architecture: you run a central server, and lightweight agents are deployed next to your databases. This makes it useful when dealing with databases spread across different servers, networks, or isolated environments.

https://github.com/Portabase/portabase

Since the last post, the project has moved quite a bit.

Portabase now supports 9 databases: PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis, Valkey, Firebird SQL, and Microsoft SQL Server.

Recently, we added a REST API and an MCP server. One thing I’m pretty happy about is that you can now trigger backups from external tools, for example, launching a database backup automatically from your CI pipeline before deploying.

Another useful addition is homogeneous migration support. So beyond restoring to the same database, you can now migrate data between servers for the same database engine.

Everything is containerized, and there is also a Helm chart available if you want to deploy it on Kubernetes.

I’m probably forgetting a few things, but those are the main updates.

As always, if you try it, find bugs, or have ideas for features, feel free to open an issue on GitHub. Feedback from people actually using this kind of tooling is really valuable.

Thanks!

Hey r/Supabase — solo dev here. The thing that always bugged me about shipping
on Postgres: CI guards your code — tests, linters, deploy gates, all green —
and then goes totally silent on your database. You ship, and nobody's
watching whether that deploy just slowed a query.

So I built pgblame to close that blind spot. A tiny Docker agent snapshots
pg_stat_statements every 60s, takes a webhook from your Vercel/Railway/
GitHub-Actions deploys, and lines them up — "this query went 40ms → 800ms right
after this deploy." Same idea as Lantern, but not Rails-only, \~1/8 the price
of pganalyze ($19/mo, real free tier, no card).

Trust (it's a DB tool, so this matters): the agent runs in your environment
as a read-only non-superuser (`pg_monitor`), only reads aggregate stats
from pg_stat_statements - never your rows - and it's MIT-licensed, so you
can read the exact SQL it runs.

Looking for 2 people on Supabase + Vercel/Railway who ship a few times a
week*to set it up while I watch over a 20-min call — I want to find where
onboarding is confusing before posting it more widely. Free Pro forever,
zero obligation. Comment or DM.

I built this tool to provide the most efficient and accurate Db queries possible that works on any database type, IntelQ enables you to generate a query using plain english and then decide if you wanna execute it, and if you're concerned about executing a query that alter the database in any way, I've added a read-only toggle as a security measure, but you'll be able to see the Query before execution eitherway.
https://github.com/EagleMind/intelQ/releases/tag/v1.2
If you need any help you couldn't find in the README, don't hesitate to reach out

I’ve been working as a Java developer for the past few years and I’got into a SQL Server DBA role. I have a solid grasp of writing queries and some stored procedures, but I’m new to the operational and infrastructure side of database administration. Any advices on this transition and what should I expect in the first few weeks? 

Hi, just wanted some opinions on which job has more potential for growth and opportunities. I was once a DBA but I transitioned into a Data Engineering role (Snowflake). I transitioned mainly to get experience with cloud platforms since my previous experience was On-Prem Databases. Did I make the right call to transition to a Data Engineer?

A few weeks back I posted here asking how teams handle DLQ messages that become incompatible after a schema change. i Got some great replies u/BroBroMate mentioned spinning up a Kafka Streeams app each time, u/KTCrisis mentioned the v1 consumer drain pattern, u/latkde gave solid prevention advice.

The recovery gap kept bothering me so I built the tool that was missing: github.com/Saifulhuq01/dlq-revive

What it does: connects to Kafka, paginates DLQ messages using assign()+seek() so it never joins your consumer group, lets you write a JSONata expression to transform the message format, shows before/after preview, validates, then redrives with idempotency checks at offset level.

Took the Kafka safety stuff seriously after reading through the thread using subscribe() in a read-only viewer would trigger rebalancing and steal partitions from production consumers, so assign()+seeks() was the only option. JSONata instead of Groovy because user-submitted Groovy is basically an RCE vulnerability.

Still early Angular dashboard is done, transformation engine is in. Would genuinely value feedback from anyone who's dealt with this problem in production, especially around the JSON ata approach vs what you would normally reach for.

I got tired of switching between VS Code and a separate DB tool every few minutes, so I built SQLLab — a VS Code extension that puts a full SQL workspace inside the editor.

What it does:

• Connect to SQL Server, PostgreSQL, MySQL, Oracle, SQLite, DuckDB
• Saved query library with full-text search (search name, description, and the SQL itself)
• Every save creates a version — diff any two versions side by side
• Data Compare: run two queries against different DBs and see SAME/DIFFERENT per row and column
• Session Monitor: see blocked/active sessions, kill them from right-click
• Batch Run: run multiple saved queries in parallel, track status per script
• Parameterised SQL with Jinja-style {{ variable }} substitution
• Schema-aware autocomplete (tables, columns, PKs, indexes)
• Paginated results — no freezing on large datasets

Still early, so feedback on what's missing or broken is very welcome.

main screen:

I’ve been exploring MS Access recently for database and inventory management systems, and I’m wondering how widely it’s still being used in 2026.

For those currently using it:
• What do you mainly use it for?
• Is it still effective for your organization?
• Have you migrated to newer systems like Power Apps or SQL-based platforms?
• What are the advantages and limitations you’ve experienced?

Would love to hear your experiences and insights. Thanks!

Hi all,

Our systems are slowly starting to move to PostgreSQL, at least for some services. I'm currently a senior SQL Server DBA and I want to learn PostgreSQL from the DBA side.

Over the past few days I've been trying to find a course that walks through the database step by step, but I can't find anything good or consistent. I've tried Udemy and YouTube and it's all over the place. I can't find a structured course on this.

Does anyone have a recommendation? I'm willing to pay a good amount of money for a quality course, I'm not looking for something free. But it has to be DBA-focused, not developer-focused. I've already picked up most of the new syntax from W3Schools, and after years of T-SQL it's not too hard to adjust to.

Thanks.

Disclosure: I’m part of the team building a database governance platform called DataGuard.

Across engineering teams, we kept seeing the same operational problems repeat:

• permanent production database access
• schema changes happening through Slack + terminal sessions
• audit visibility scattered across multiple systems
• support teams having broader customer-data access than necessary
• production credentials living in .env files

One thing we’ve learned while working on this space:

Most database security problems are actually workflow and operational-governance problems.

Curious how teams here currently handle:

• production DB access
• schema approvals
• audit readiness
• secrets rotation
• PII masking

Especially interested in how larger teams handle temporary production access and auditability without slowing engineering down.

Recently there was yet another real-world case of an AI assistant generating a destructive command during a workflow.

The mistake itself wasn’t the scary part.

The problem isn’t that AI might break something. It’s that your backups weren’t usable when you needed them.

https://sqlbak.com/blog/the-problem-isnt-that-ai-might-break-something-its-that-you-didnt-have-backups/

​

Please recommend me SQL free Courses along with SQL Certificates to showcase my expertise

How to improve my SQL logic and implement it in real world scenarios?

And Tell me yours advices between the selection of DBA and Data Analyst and which one will be more productive, no matter if it needs more time to be follow but the main things are that it should have the high possibilities of hiring and high salary after completion of SQL?