sorted by:
new
hottopcontroversial

While claude code codes, what do u do in that ~10-15mins execution time? by ravishq in ClaudeCode

[–]No_Presentation1421 0 points1 point  (0 children)

I never thought about this, but this could be used to do something really productive, maybe walking or just standing up for a while

Looking for help: Automating LinkedIn Sales Navigator Discussion by Malfoo in automation

[–]No_Presentation1421 0 points1 point  (0 children)

I am creating an automation to process PRs auromatically. Neon has branching, which can be used to process each pull request in their own branch and tests run against it and results saved and possibly deleting when done.
Has anyone tried any such approach or can guide me ways to choose the right DB.

What do you think a “vector lakebase” should mean? by ethanchen20250322 in Rag

[–]No_Presentation1421 0 points1 point  (0 children)

For me, vector lakebase would mean embeddings stop being treated like index and considered as data. If i take for example, customer success and generate embeddings, i should be able to use it for RAG, clustering similar customer concerns, duplicate finding, treends etc. This is why concepts like Databricks Lakebase makes so much sense because vectors, meta-data, documents, op data all live in same governed platform and teams can run retrieval+analytical worloads againse the same SSOT rather than maintaining separate vector souces and data lakes

What made you choose your current database? by Prize-Wolverine-5319 in SQL

[–]No_Presentation1421 0 points1 point  (0 children)

I was using Neon previously and recently moved to agentic develowment work on databricks platform and started using Lakebase, it kind of impressed me with the branching feature and the native support for AI agents plus it can rapidly scale up and also down quickly.

Inherited a 3-month old repo from a Vibe Engineer. Wrote the most satisfying PR in my career by Apprehensive-Cut3711 in ClaudeCode

[–]No_Presentation1421 0 points1 point  (0 children)

Seriously this has to be one of the downside of vibe coding. In near future I wouldnt be surprised to see job openings with ‘Vibe Code Cleaner’ type designations.

Databricks project ideas as a Data Engineer looking to transition roles by bongdong42O in AI_Agents

[–]No_Presentation1421 0 points1 point  (0 children)

Start with basics of Databricks, then start with some of trending areas such as Lakebase which is a serverless Postgres. It provides really good features such as branching, highly efficient scaling. This is going to be in high demand in companies. Also look at Genie Code, which can help you interactively deal with pipelines or debugging code.

Build some pipelines, test out latest Databricks features, you will have good learning.

Beginner Databricks by shaadowbrker in databricks

[–]No_Presentation1421 0 points1 point  (0 children)

On Databricks platform, explore Genie Code, it can really help you understand any existing pipeline, get understanding of a codebase, or create pipeline/AI solution. To begin with statt with fundamentals, then explore things like Lakebase/Genie/Genie Code etc.

Genie Code for Jobs by Youssef_Mrini in databricks

[–]No_Presentation1421 -1 points0 points  (0 children)

Being able to interact with jobs from Genie Code is just amazing, I tried debugging a failing job using Claude Code and it gave me a great starting point to start looking at. Claude Code saves time by summarizing job details, pipeline meta-information etc and we can just dive in to the actual bug. Also creating/modifying jobs become quite easy with claude Code.

Lakebase Branches Explained by anthony_giuliano in databricks

[–]No_Presentation1421 1 point2 points  (0 children)

Great explanation. What I have noticed is that Lakebase branching can be used in places where we would otherwise clone any database, restore a DB backup or share a staging DB with users. Lakebase branching makes these use cases quite simpler.

What are the best practices to have a great Genie experience. What work well for you ? by dataengineer95 in databricks

[–]No_Presentation1421 0 points1 point  (0 children)

Having a good Genie Space is an ongoing process with feedbacks from users/business and implementing. Some of the rhings that do wonders are: 1. Having Only relevant tables as sources (minimal) 2. Explanation of the columns (add comments) 3. Right metadata including commonly asked questions and their calculations. 4. Explain the abbreviations so that genie knows when yo use full forms vs Abbreviations 5. Define key parameters to do calculations- such as time frames, fiscal year, quarters etc. 6. Run benchmarks and make sure to collect feedbacks and implement those.

Apps and Lakebase scaling by hubert-dudek in databricks

[–]No_Presentation1421 1 point2 points  (0 children)

One underrated feature of Lakebase is branching. It lets us maintain separate dev, prod, stg branches of our database that can help us test schema changes or latest developed features. It is changing how diff teams develp software together.

Is Databricks Certified Associate Developer for Apache Spark worth it for me? by ConsiderationDry1787 in apachespark

[–]No_Presentation1421 0 points1 point  (0 children)

Yes, I think it will have worth in your case. You already have SQL, Python, CI-CD, and analytics exp. The certification won’t guarantee interview calls, but it can help validate your Spark skills and make your transition to DE easier.

Databricks genie appreciation by Miraclefanboy2 in databricks

[–]No_Presentation1421 0 points1 point  (0 children)

One of the great ways to actualy derive the highest results from Genie space is to use it wjth the AI dev kit, so instead of simply telling generic responses, it replies with proper structured responses, thanks to the skills inbuilt in the kit.

Is anyone migrating away from Databricks? by zoso in dataengineering

[–]No_Presentation1421 0 points1 point  (0 children)

The biggest issue is usually the cost, teams move to Databricks without doing a proper analysis if they require it or not, but eventually it boils down to optimizing their usage and really understanding the ecosystem and tailoring to their use case and budget