Beyond the lakehouse: Fundamental's NEXUS bypasses manual ETL with a native foundation model for tabular data
The deep learning revolution has a curious blind spot: the spreadsheet. While Large Language Models (LLMs) have mastered the nuances of human prose and image generators have conquered the digital canvas, the structured, relational data that underpins the global economy — the rows and columns of ERP systems, CRMs, and financial ledgers — has so far been treated as just another file format similar to text or PDFs.That's left enterprises to forecast business outcomes using the typical bespoke, labor-intensive data science process of manual feature engineering and classic machine learning algorithms that predate modern deep learning.But now Fundamental, a San Francisco-based AI firm co-founded by DeepMind alumni, is launching today with $255 million in total funding to bridge this gap [...]
Rating
Innovation
Pricing
Technology
Usability
We have discovered similar tools to what you are looking for. Check out our suggestions for similar AI tools.
Fundamental emerges from stealth with first major foundation model trained for tabular data
The deep learning revolution has a curious blind spot: the spreadsheet. While Large Language Models (LLMs) have mastered the nuances of human prose and image generators have conquered the digital canv [...]
Databricks' serverless database slashes app development from months to days as companies prep for agentic AI
Five years ago, Databricks coined the term 'data lakehouse' to describe a new type of data architecture that combines a data lake with a data warehouse. That term and data architecture are n [...]
Databricks says it solved the decades-old data pipeline problem that's been slowing AI agents
For decades, data professionals have struggled with the challenge of managing both operational and analytical databases in a unified approach that doesn't introduce latency and performance degrad [...]
The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next
The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymore; agentic AI [...]
Databricks set to accelerate agentic AI by up to 100x with ‘Mooncake’ technology — no ETL pipelines for analytics and AI
Many enterprises running PostgreSQL databases for their applications face the same expensive reality. When they need to analyze that operational data or feed it to AI models, they build ETL (Extract, [...]
The 'last-mile' data problem is stalling enterprise agentic AI — 'golden pipelines' aim to fix it
Traditional ETL tools like dbt or Fivetran prepare data for reporting: structured analytics and dashboards with stable schemas. AI applications need something different: preparing messy, evolving oper [...]
H2O.ai launches tabH2O, a foundation model that makes predictions from tabular data without any training
H2O.ai has unveiled tabH2O, a foundation model purpose-built for tabular data that can generate high-accuracy predictions from structured datasets using a single API call, with no model training requi [...]
The modern data stack was built for humans asking questions. Google just rebuilt its for agents taking action.
Enterprise data stacks were built for humans running scheduled queries. As AI agents increasingly act autonomously on behalf of businesses around the clock, that architecture is breaking down — and [...]