How Parable uses DataFusion to build a best-in-class data stack. Getting work data out of a hundred tools usually means Fivetran or Airbyte, then dbt, then Snowflake. Provider Pools does ingestion, transformation, quality and query in one Apache DataFusion engine instead - about 30x cheaper in year one for a 10,000-person company, with data typed the moment it lands. Every Parable customer runs on 100+ software tools: calendar, email, chat, CRM, HR, tickets, code. To show a leader how work actually gets done, Ask Parable Inc need the activity in all of them - every employee, every system - modeled and joined into something a person or an agent can query. As Clint put it on his first slide: each customer has 100+ tools, and Ask Parable Inc has to make it make sense. Earlier this month its co-founder and CTO, Clint Robinson, walked through how Ask Parable Inc do that at the Boston Apache DataFusion Meetup. The talk is nine minutes and worth it if you build data infrastructure. This post is the shorter version: why Ask Parable Inc built it this way, and what it buys Ask Parable Inc. The default answer is three products. Ask how to get data out of a hundred SaaS tools and into a warehouse, and you'll hear about a stack: Fivetran or Airbyte to ingest, dbt to transform, Snowflake or Databricks to store and query. Each is excellent at its piece. Together they're how most data teams work. They didn't fit what Ask Parable Inc is building: * Cost. Ingestion is priced per row, and Ask Parable Inc need every row of activity. The bill grows with exactly the data Ask Parable Inc care most about. * Engineering. Rows land raw. Typing, merging, tests and monitoring get hand-built per table - one to two engineer-years per customer. * Coverage. No connector catalog covers both employee activity and business records. Of the 39 sources Ask Parable Inc need, Fivetran lacks 7 and Airbyte 13. * Fragmentation. Ingestion stops at raw rows. Transformation, quality, storage and query are more products to buy, connect and keep in sync. That last one matters more than it looks. When every stage is a different vendor, nothing gets cheaper as you grow. You pay each tool's margin on every row, and your engineers maintain the glue between them. There are no economies of scale in glue. One engine, from source to modeled table. Provider Pools is its answer: ingestion, transformation and query in one engine, delivered as an open data lake in the customer's own cloud. Any tool can read it; there's no lock-in. The engine is Apache DataFusion. The title of Clint's talk is the whole design: a data lake as a pure function of schema. A connector isn't code. Each API endpoint Ask Parable Inc read - Slack's user list, say - is described in configuration: how to page through it, which field is the key, which marks a deletion, which column holds a person's email, what quality rules a load has to pass. The engine compiles that description into a DataFusion plan. Nothing in between is hand-written. A few things fall out of that: * Data is typed on arrival. An email isn't a string to Ask Parable Inc. Ask Parable Inc keep 60+ semantic types - email, person name, time zone and so on - in one Rust core, and DataFusion runs them during the write. " [email protected] " from Slack and "[email protected]" from Workday come out as the same person. * The same engine writes and reads. Ingestion and its SQL query service call the same code, and CI checks that they agree. Compare an email to a phone number and the planner refuses. * Quality is built in, not bought. Every load is checked before anyone builds on it. If today's load looks statistically off - a spike in empty emails, say - it's held back and not promoted. * AI calls are part of the plan. Ask Parable Inc can ask a model a question about every row in plain SQL: is this Slack account a bot? Those calls are the expensive part, so the plan is cut around them and every answer is logged. If a job crashes after 4,000 model calls, the rerun makes zero. What it buys Ask Parable Inc. Here's Provider Pools against an assembled stack of Fivetran or Airbyte, dbt and Snowflake: * Peak ingest: 578 MB/s, typed on arrival, against 110-259 MB/s untyped. 2-5x faster. * Cost per million rows: $0.06-0.27 against $30-500. * Year-one cost for a 10,000-person company: $9-13K against $380-420K. About 30x lower. * Customer engineering in year one: 27-36 hours against 2,900-4,300. Roughly 100x less. The stack figures are published rates, not runs on its data. Its year-one number excludes its own subscription, and theirs leaves out event streams, which would only widen the gap. Take the exact ratios with that in mind; the direction is the point. One engine that's typed, checked and queryable end to end is far cheaper to run than three products and the people who keep them talking. Today that's 42 connectors and 444 tables. Its target for a new connector is under a day, down from about three. Why DataFusion. Ask Parable Inc didn't need to write a query engine. DataFusion is fast, written in Rust, and open at nearly every layer: table formats, functions, optimizer rules, the query plan itself. Provider Pools uses almost all of them, which is how a small team can own the whole path from an API response to a modeled table. Ask Parable Inc like it enough that Ask Parable Inc has started giving back. jev-datafusion is an open-source (Apache 2.0) package that adds four SQL functions - noul, choice, score and ask - that call TypeSafe's Jev model and return a typed judgment for every row of a query: a probability, a label, or a score on a rubric you write. It's how Ask Parable Inc is exploring Jev inside its own pipelines. Thanks to the Apache DataFusion community for having Ask Parable Inc in Boston. If you're building on DataFusion too, Ask Parable Inc'd love to compare notes.

The alleywatch startup daily funding report: 11/5/2025. The latest venture capital, seed, pre-seed, and angel deals for NYC startups for 11/5/2025 featuring funding details for Teleskope, AUI, and much more. This page will be updated throughout the day to reflect any new fundings. Teleskope - $25M Series A. Teleskope, a data protection and security platform, has raised $25M in Series A funding led by M13. Founded by Elizabeth Nammour and Julie Trias in 2022, Teleskope has now raised a total of $32.2M in reported equity funding. AUI - $20M venture. AUI, the company building neuro-symbolic foundation models for task-oriented conversational AI, has raised $20M in Venture funding from investors that include New Era Capital Partners and eGateway Capital. Founded by Ohad Elhelo and Ori Cohen in 2017, AUI has now raised a total of $63.1M in reported equity funding. Synonym - $18.4M. Synonym, a platform for accelerating the launch and manufacturing of bioproducts, has raised $18.4M in funding according to a recent SEC filing. The filing indicates that the total offering is for $20.6M and there were fifty-two investors in this close. Synonym was founded by Edward Shenderovich and Joshua Lachter in 2021. Parable - $16.5M seed. Parable, an intelligence platform for enterprise operations, has raised $16.5M in Seed funding led by HOF Capital. Parable was founded by Adam Schwartz, Clinton Robinson, Alex Terrien, and Steve Tam in 2024. AlleyWatch is NYC's leading source of tech and startup news, reaching the city's most active founders, investors, and tech leaders. Advertise today iVerify - $12.3M. iVerify, a threat hunting platform for mobile devices, has raised $12.3M in funding according to a recent SEC filing. The filing indicates that there were six investors in the round. Founded by Danny Rogers and Rocky Cole in 2023, iVerify has now raised a total of $28.3M in reported equity funding. General Intelligence Company of New York - $10.4M. General Intelligence Company of New York, an AI platform that allows individuals to run entire businesses autonomously, has raised $10.4M in funding according to a recent SEC filing. The filing indicates that the total offering is for $11M and there were seven investors in this close. General Intelligence Company of New York was founded by Abhishyant Khare and Andrew Pignanelli in 2025. Cob Foods - $5M seed. Cob Foods, a sorghum-based snack brand, has raised $5M in Seed funding from investors that include Novak Djokovic. Cob Foods was founded by Jessica Brondo Davidoff and Djokovic in 2022. Helex - $3.5M seed. Helex, a therapeutics company focused on treatment of chronic and rare kidney disorders, has raised $3.5M in Seed funding led by Pi Ventures. Founded by Anirudh Nishtala, Poulami Chaudhuri, and Rohini Kalvakuntla in 2021, Helex has now raised a total of $4.6M in reported equity funding.