Why you should move your ETL stack to Modal
modal.com
Why you should move your ETL stack to Modal
(1) The separation between storage and compute , as encouraged by data lake architectures (e.g. the implementation of P would look different in a traditional database like PostgreSQL, or a cloud warehouse like Snowflake). This architecture is the focus of the current system, and it is prevalent in most mid-to-large enterprises (its benefits that be... See more
Jacopo Tagliabue • Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie.
Top considerations when choosing foundation models
Accuracy
Cost
Latency
Privacy
Top challenges when deploying production AI
Serving cost
Evaluation
Infra reliability
Model quality
Accuracy
Cost
Latency
Privacy
Top challenges when deploying production AI
Serving cost
Evaluation
Infra reliability
Model quality
Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.
Depending on your company’s specific requirements, for example, a specialized data profiling or data movement tool might be needed to complement a more general-purpose ETL tool.
Steve Williams • The Profit Impact of Business Intelligence
You’ve got a vector database that has all the right database fundamentals you require, has the right incremental indexing strategy for your use case, has a good story around your metadata filtering needs, and will keep its index up-to-date with latencies you can tolerate. Awesome.
Your ML team (or maybe OpenAI) comes out with a new version of their... See more
Your ML team (or maybe OpenAI) comes out with a new version of their... See more
6 Hard Problems Scaling Vector Search
The ETL process for getting data out of transactional systems and into an EDW was always a bit burdensome, but with massive volumes of high-velocity data it becomes a real problem.
Thomas H. Davenport • Big Data at Work: Dispelling the Myths, Uncovering the Opportunities
uniflow provides a unified LLM interface to extract and transform and raw documents.
- Document types: Uniflow enables data extraction from PDFs, HTMLs and TXTs.
- LLM agnostic: Uniflow supports most common-used LLMs for text tranformation, including
- OpenAI models (GPT3.5 and GPT4),
- Google Gemini models (Gemini 1.5, MultiModal),
- AWS BedRock models,
- Huggingf