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DPF

Turns natural-language descriptions of raw files into generated parsing pipelines that land data as Apache Iceberg tables.
At a glanceswept Oct 23, 2026

not read yet

Use

For
Data teams and analysts who want to build ingestion and transformation pipelines from natural language instead of hand-coded ETL.
Used for
  • turn raw CSV, JSON, XML or Excel files into queryable data
  • generate parsing and transformation pipelines from plain-language descriptions
  • land transformed results in open Apache Iceberg tables
Worth knowingcomputed from what we read · each with its source
How a program reaches itfound, not claimed · read Oct 24, 2026
  • MCP servercom.dpf-it/mcp-serververified in the MCP registry
  • Stateanswers, 18 toolschecked 2026-10-24
  • Address to connectapi.dpf-it.com/mcp

MCP server details →

This card as JSON, for your agent →

Each line is something a program found when it went looking: the registry entry, the server's own answer, a spec that parses. A missing line means we did not find it, not that it does not exist.

Alternatives in Salesby use, among tools still maintained

Figure and bar: visits/mo, estimate · year: online since

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beta

Three tools from the Atlas for the job you describe, each with what fits and what does not.

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Questions this page answers
  • What is DPF?

    Turns natural-language descriptions of raw files into generated parsing pipelines that land data as Apache Iceberg tables. It is listed in the Atlas under Sales.

  • Who is DPF for?

    Data teams and analysts who want to build ingestion and transformation pipelines from natural language instead of hand-coded ETL.

  • Do people use DPF?

    Little sign of use.

  • What are alternatives to DPF?

    In Sales, by use: ColdIQ, Apollo.io, Bardeen IT, Lemlist Agent, Oppora AI, Enginy.

How we read a tool

No votes, no reviews, no vendor claims. Every week a crawler reads each tool's own site and a few public registries, and the words on the card are bands over what it read.

  • Use: visits to the site (estimated), installs from npm and PyPI, presence in Chrome's usage report.
  • Activity: the newest release on GitHub, npm or PyPI; the newest dated page on the site; open roles on a public jobs board.
  • Price: the vendor's own pricing page, read with the date. A figure is printed only when it is on that page.
  • The line and the use cases are written by us from the homepage, one tool at a time, and refused when they repeat the vendor's marketing.

How AI assistants read each site — the reading vendors ask us about — is on each tool's own visibility page.

How we read every tool → · By INITE Atlas Desk