My team (retail snalytics) is analyzing genie for selfserve analytics, but we r regularly seeing high latency and timeout when it parses complex business logic or hits large Delta tables.
For prod: how are u keeping response times down ?
My team lead has asked me to explor Lakebase for a RAG app where documents will get updated frequently .Hav you used it in prod? Curious how it compares to a dedicated vector DB for indexing and retrieval latency.
I am using Databricks platform for poc
My team is experimenting with Genie Code on Databricks pltform. Has anyone used it to analyze existing Spark jobs and suggest thing like broadcast join , partition pruning , or reducing shuffles ?
How can i make it suggest things with validated reasoning to make our produ tivity more
I am keen to know how reliable genie ‘s Spark optimization or suggestions in practice and how best to use them.
If a metric can be calculated differently depending on filters /time window, do you prefer to hav a semantic layer/ metric view, or hardcoding the logic in each query or dashboard ?
Curious how ppl keep Ai generated analytics consistent and trustworthy. Ps i am setting up Genie agent for this and want to implement the reccomendations
Have you experimented combining Rag with tools such as Genie Code and Neon?
Rag handles unstructured knowledge, genie handles governed structured data and lakebase handles persistent applincaton state,
So i am curious, doees this architecture look good for production and what can some of the bottlenecks be down the line ?
Have u benchmarked writing to Neon over JDBC conn with diff numPartitions/connection counts?
I was exploring how Neon behaves when Spark starts opening a lot of parallel connection, does it improve throughput, or do we hit connection /IO bottlenecks quickly?
Curious what people have found to work well in practice.
Which area dou think is suited for IT professionals for future 10-15 years and procimity to trch parks and schools?
How do u handle branching when the schema is changing frequently during agent/app development work. Do you keep separate branches per feature or agent, or rely on migrations against a shared database?
Wondering what works best in practice without making the overall setup messy.
How do u handle query performances when the same tables are being hit by both app queries and AI generated SQLs from any AI tools such as Codex, CLaude, Genie etc
Curious if you separate workloads or optimize at the query level.
Have you used lakebase as the serving Db for an ML application?
I am curious to know how it holds up for real time feature lookups or for inference workloads, specially wrt latency and concurrency.
Any gotchas you felt compared to usual postgres setup?
have u used Genie Space on top of federated HMS at scal
I see noticable latency on queries and I was curious if there is any less obvious ways to improve performance for ex metric views, caching, table design, or Genie-specific opts.
Would love to hear what has worked for you.
I am looking to buy a 3bhk in sarjapur road, Varthur etc areas.
Budget around 1.7-1.85 Cr
I have done EOI in Abhee New dimension currently
Also exploring resale options in Prestige city Avalon park.
Can someone please guide me because both these are around the same price 1.8Cr for Abhee 1.9-1.95 for prestige citt.
What should i do? Are there any more options i should explore.
Requirement: Decent society, good areas, basic amenities like park and all for kids.
I had a question, for longer running agents, what can be a good pattern to store conversation state, tool result, and intermediate state in Neon without database becoming a bottleneck.
If you can share what patterns you use in production, it could be of help to me. I am doing a POC inthis area.
Ps, Any other DB works as well, it should just be managed.
Have tou used Genie Code to create a Genie Agent? I wanted to understand if it is possible to uae Genie Code itself to gather context from our data landscape, use it for creation of instructions, metric views and subsequently a fully operarional genie Space.
I have tried it and setup one, but the the responses seem to always time out, or dont have precision. Same question provides different answers sometimes.
Claude Code is not available any more in my team due to cost factor, so i am trying this approach.
For teams using federated HMS tables instead of UC, how are you setting up Genie agent?
—> do you add only curated tables/views to the agent OR use metric views fully to define the business layer?
I just want accurate and consistent responses for businee users, any best practives would be helpful.
I have lately been using Genie on Databricks to create agents (spaces) for business usrs and trying to understand how can i structure Metric Views for better and more consistent answers. Genie specific best practices would be really helpful.
My setup is currently HMS with federated tables, not Unity Catalog. (Due to business reasons, will migrate in next qtr).
Curious how others are setting this up.
I have been using Lakebase sice 2 weeks for developing a serving low latency layer for data in Delta. To build new features we currently make a new branch and later merge ti main.
What i have been observing is the scehema drifts across branches with Lakebase. How do people usually handle such scenarios? To keep branches in sync.
Maybe some real life examples can help.
I have just setup a Genie agent on Databricks and despite the instructions and sample SQLs i dotn get production level responses.
How can I make Genie more and more accurate to roll out to business users?
I have fone through documentations, so looking for real life experiences and help
I have been always taking backups of DB before performing any major changes, and was recently creating an app for displaying customer history retrieved from lakebase as a source.
I felt instead of taking a backup, i can just use a new branch.
This is sort of a new paradigm with databases, i come from OLTP background. Anyone else feels the same?
Apologies if this is already asked in the channel.
Wanted to understand with Neon database, how does the compute get handled. If we have more branches, does that mean the cost goes up?
Does anyone has insights on it?
Sorry if this is asked previously (I didnt find it)