Something that saved me after a scheduled job wrote garbage to prod: Neon (managed Postgres) does point-in-time restore by branching from any past timestamp, and Time Travel Assist lets you query the db as it was right before you commit to it. So instead of restoring a nightly dump you just branch at 2 min before the bad write and diff.
A pattern thats been working for me instead of a nightly reverse-ETL script copying aggregates from the warehouse into the app db: Lakebase synced tables keep a Postgres table continuously in sync with a source Delta table, so the app reads fresh computed values with no cron to babysit. Am I missing something else out there?
How have people been wiring database branching into their preview-deploy automation?
I've been putting per-preview databases into CI and Neon's been handy for it, the Vercel integration spins up an isolated database branch for every preview deploy so each PR tests against its own copy of the data, then tears the branch down on merge. If so, how are you seeding test data into each fresh branch?
If your agent needs to answer data questions, the Databricks Genie Conversation API lets you POST a plain-english question over REST and get back the generated SQL plus results, with thread context. The schema and joins are in the Genie space instead of your prompt, so you're not resending table metadata every call. Anyone wiring NL-to-SQL into agents this way, hows it holding up on messy schemas?
When a new data source shows up I used to hand-write the whole ingest+transform each time. Genie Code (coding assistant) can author that pipeline from a plain-english spec and make agentic edits across the notebook, so you review and tweak instead of writing boilerplate. Anyone leaning on NL-to-code for the repetitive pipeline glue yet, and what are you using?
I wired up a daily metrics bot and the thing that actually moved accuracy wasnt the model, it was curating the Databricks Genie Space. I added a few certified example SQL queries and plain-language instructions & it stopped guessing joins and returns the same answer every run.
Providing NL models and agents just a few guidance examples seems to work very well.
I got burned by an automation that half-ran against prod and left a mess, so now i spin up a throwaway database branch per test run and toss it after. Neon makes that practical, its Postgres branching clones your real schema and data into an isolated copy in a couple secs, so the automation writes to the branch and prod never sees a bad run. Anyone else doing branch-per-run, or just pointing at a shared staging box and hoping?
If your automation only touches its db in short bursts (a nightly job, a webhook firing a few times a day), an always-on Postgres is mostly paying to sit idle. Lakebase, the managed Postgres on Databricks, autoscales and scales compute to zero when idle then wakes on the next query, plus copy-on-write branches let you spin a throwaway db per test run. Anyone moved bursty jobs to scale-to-zero postgres, hows cold start feel in practice?
If your agent needs to answer questions over real tables mid-workflow, the Databricks Genie Conversation API is worth a look, you POST a plain-english question and poll back the generated SQL plus the actual rows, so the model isnt guessing numbers from its context. The thread stays stateful too, so followups reuse the earlier question without you re-stuffing everything. Anyone here wiring NL-to-SQL into their agents this way?
In the Lakeflow Pipelines Editor, Genie Code agent mode can plan and generate a whole medallion pipeline from a plain-english prompt, ingestion through transforms, editing multiple files and showing you the diff in each one before you accept it. Nothing runs without your confirmation so its not a black box you just trust blindly. Anyone here using it to stand up pipelines instead of hand-coding them?
Most reverse-ETL I see is a cron job reading a warehouse table and hammering upserts into the app's Postgres, which drifts and breaks quiet. Lakebase synced tables flip that: you declare the source and it keeps a read-optimized Postgres copy continuously updated, so your app reads fresh analytical output without you owning a sync job. Anyone feeding an app with synced tables instead of a hand-rolled upsert loop?
I have been moving data-quality alerts from "page a human" to "have something investigate first." I am using agent mode in Databricks Genie Agents: hand it a vague question like "why did signups drop" and it does multi-step reasoning and hypothesis testing across your tables, then returns a report with citations instead of a threshold ping. Better Slack message than "metric is red." Anyone else letting an agent take the first pass, or still a human step for you?
If you have automations writing to a Postgres, Neon's instant restore is a decent safety cgeck - you can revert a root branch to any timestamp inside your history window, down to the millisecond, and it takes a few secs instead of a full dump restore. Nice part is it doesn't throw away the bad state, it renames the pre-restore branch to {branch}old{timestamp} so you can still go read what your script actually did. Whats everyone else doing for a rollback path when an unattended job writes garbage?
I've automated a lot of Slack responses with our Databricks setup. When people keep DMing you for numbers, the Databricks Genie app for Slack is a good way to automate that away: you u/Genie in a channel and it answers in thread with the sources and follow-up prompts. The useful bit is a channel owner can pin one specific Genie Agent to that channel so everyone gets consistent answers off the same datasets, and replies can be set public or private to just the asker.
Like I said earlier, automating email drafts/replies has been my #1 automation. If you're using some other ones like that, let me know because I'm trying to do this across all surfaces.
One automation that saved me a ton of flaky test pain used Neon's Vercel integration that creates an isolated copy-on-write database branch for every preview deployment, so each PR gets its own real Postgres with prod-like data in secs and it's torn down when the branch closes. No more shared staging db where two previews stomp each other. You can wire the same branch-create/drop into any CI via their API too. Anyone else auto-provisioning a db per PR, or still sharing one staging instance?
An automation that killed a whole cron job for me: Lakebase synced tables. Instead of writing a reverse-ETL script to push a curated analytics table back into an app's Postgres, you point a synced table at the source and pick a mode, snapshot, triggered, or continuous, and a managed pipeline keeps the Postgres copy fresh (continuous applies inserts/updates/deletes in near real time). So the app reads current data at Postgres latency with no glue code to babysit.
I am getting almost my entire workloads automated, crazy awesome times.
One automation win that is saving me time lately: Genie Code can run agentic tasks right inside the Databricks workspace, so instead of hand-writing an ETL job you describe the transform and it drafts the pipeline code across notebooks and pipelines, then you review and run it. What'll bite you is treating the output as final, imo you still eyeball the joins and edge cases. I had this similar to visual builder tools in the past, but they were never as precise.
Saved myself a ton of manual reporting work with the Databricks Genie Conversation API: you POST a plain-english question, poll for the result, and get back the generated SQL plus the rows your workflow can act on. It supports service-principal auth and runs under Unity Catalog perms, so an unattended job only touches data that identity is allowed to see. Anyone else automating the NL-to-data step this way, or still hard-coding the SQL?
Hello,
I thought that I remembered having family benefit for Upfront seating, but now I see that the chart and the system only gives it to the primary. Does anyone else remember if it used to be group benefit?
I am not sure when this changed, but the Frontier vouchers are now 12 month expiration instead of 6 months. I tried searching and didn't see an update in the top results so wanted to post here.