steampipe VS evadb

Steampipe is a tool for querying cloud APIs and other data sources using SQL in a zero-ETL manner.

Readers may also enjoy Steampipe [1], an open source CLI to live query Google Sheets [2] and 140+ other services with SQL (e.g. AWS, GitHub, etc). It uses Postgres Foreign Data Wrappers under the hood and supports joins etc across the services. (Disclaimer - I'm a lead on the project.)

1 - https://github.com/turbot/steampipe

be mindful of its AGPLv3 https://github.com/turbot/steampipe/blob/v0.21.8/LICENSE (AFAIK v0.4.3 is the last MIT release https://github.com/turbot/steampipe/blob/v0.4.3/LICENSE ) and the actual providers are Apache 2 <https://github.com/turbot/steampipe-plugin-aws/blob/v0.131.0...> (but I don't know if provider drift makes them compatible with 0.4 or not)

iasql seems to be AWS only, but good for them for taking this on:

In a prior post I showed how to install Steampipe in AWS CloudShell to instantly query over 460+ resource types from your AWS APIs using SQL, and another post on how to use the Steampipe AWS Compliance mod to assess over 25+ security benchmarks across your AWS accounts.

Readers may find Steampipe's [1] AWS Thrifty Mod [2] useful. It will automatically scan multiple accounts and regions for 50 cost saving opportunities - many of which are looking for over-provisioned or unused resources. For example, it's crazy how much you can save by doing things like just converting your EBS volumes to the newer gp3 type. Combine with Flowpipe [3] to automate checks and actions. It's all open source and extensible.

1 - https://github.com/turbot/steampipe

Steampipe [1] is an open source project [2] that includes an embedded Postgres to instantly query cloud, code & more with SQL. This release expands our plugin ecosystem [3] to be a full Zero-ETL platform. Steampipe plugins can now run natively in your own Postgres as Foreign Data Wrappers [4], as SQLite extensions [5] or as simple data export tools [6]. Please give it a try, we'd love your feedback and contributions!

1 - https://steampipe.io

Hey friends!

We have built an app for getting insights about your favorite GitHub community using large language models.

The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.

This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.

Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!

We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.

We’ve been working on this app for a month now and are excited to open source it today :)

Some useful links:

* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...

* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/

* EvaDB - https://github.com/georgia-tech-db/evadb

Please let us know what you think!

The discord link seems to be not working. Just a heads up.

The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....

Once the function is loaded, it can be used in queries in this way:

  SELECT id, Yolo(data)

Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?

[1] https://github.com/georgia-tech-db/eva

Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs.

[1] https://github.com/georgia-tech-db/eva/tree/master/docker

EVA AI-Relational Database System; build simpler and faster AI-powered apps