Alternatives
Products that do what DataForge does
Distributed ETL pipeline engine.
- 1IB
Every data pipeline job I had to tackle required quite a few components to set up: - One tool to ingest data - Another one to transform it - If you wanted to run Python, set up an orchestrator - If you need to check the data, a data quality tool Let alone this being hard to set up and taking time, it is also pretty high-maintenance. I had to do a lot of infra work, and while this being billable hours for me I didn’t enjoy the work at all. For some parts of it, there were nice solutions like dbt, but in the end for an end-to-end workflow, it didn’t work. That’s why I decided to build an…
2024 · github.com
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Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent…
2024 · github.com
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I've always been curious about how SQL engines actually work. So I built a minimum viable SQL engine in Go. - Supports CSV files as tables - Supports SELECT, FROM, WHERE, LIMIT It's very simple: 1. Parses query string 2. Converts it into an AST representation 3. Executes the query against the CSV 4. Returns the results
2024 · github.com
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Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)
2023 · chainforge.ai
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I'd like to share a project I've been working on for the past few months. It's a distributed workflow engine written entirely in Go. Some highlights: * Tasks are executed in a Docker container * Can run stand-alone or distributed * Highly extensible * Able to enforce limits (CPU/RAM) per task * Web UI Would love the get your feedback on it, and find out if this could be useful.
2023 · github.com
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2022 · github.com
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We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com
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Data pipelines as code — self-hosted, zero black boxes
13d ago · etlfunnel.com
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Hi HN! We are Ashish and Armend, founders of GlassFlow. We just launched our open-source streaming ETL that deduplicates and joins Kafka streams before ingesting them to ClickHouse https://github.com/glassflow/clickhouse-etl Why we built this: Dedup with batch data is straightforward. You load the data into a temporary table. Then, find only the latest versions of the record through hashes or keys and keep them. After that, move the clean data into your main table. But have you tried this with streaming data? Users of our prev product were running real-time analytics…
2025 · github.com
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Just launched DataFuel.dev on Product Hunt last Sunday, and I landed in the top 3! I built this API after working on an AI chatbot builder. Scraping can be a pain, but we need clean markdown data for fine-tuning or doing RAG with new LLM models. DataFuel API helps you transform websites into LLM-ready data. I've already got my first paying users. Would love your feedback to improve my product and my marketing!
2024 · datafuel.dev
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I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You…
Apr 2026 · stream-sock-3f5.notion.site
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2022 · mage.ai
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