---
title: "Discussion on \"How IFCO Cut a 7-Hour dbt Job to 2 Hours 20 Minutes on Databricks\" | Hashnode"
description: "Discussion on \"How IFCO Cut a 7-Hour dbt Job to 2 Hours 20 Minutes on Databricks\". A data pipeline does not need a radical rewrite to become dramatically faster. Sometimes, the biggest gains come from making an incremental model truly incremental—and finding out what the query engin"
url: "https://hashnode.com/posts/how-ifco-cut-a-7-hour-dbt-job-to-2-hours-20-minutes-on-databricks/6af3681b5454f0d485c2adb9"
---

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# [How IFCO Cut a 7-Hour dbt Job to 2 Hours 20 Minutes on Databricks](https://aidatalegends.hashnode.dev/how-ifco-cut-a-7-hour-dbt-job-to-2-hours-20-minutes-on-databricks?utm_source=hashnode&utm_medium=feed)

A data pipeline does not need a radical rewrite to become dramatically faster. Sometimes, the biggest gains come from making an incremental model truly incremental—and finding out what the query engin

1d ago · 4 min read · aidatalegends.hashnode.dev  
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## Written by

[Aritra Chakraborty](https://hashnode.com/@aidatalegends)

@aidatalegends · Kolkata, India

  
[View profile](https://hashnode.com/@aidatalegends)

Writing about practical AI and data engineering at aidatalegends.com

I write AI Data Legends: hands-on guides to LLMs on Amazon Bedrock, Databricks, vector search, AI agents and workflow automation. Each post here is an excerpt; the full articles with diagrams, code and sources live at aidatalegends.com.

## Responses 1

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[StockPearl](https://hashnode.com/@stockpearl)

[19h ago](https://hashnode.com/posts/how-ifco-cut-a-7-hour-dbt-job-to-2-hours-20-minutes-on-databricks/6af3681b5454f0d485c2adb9/comment/6af3fd454d90c3845e288fd7)

The window sizing tradeoff is the part I've struggled with too. On a filings dataset I work on, corrections can show up months later when a company amends a report, so a fixed lookback either misses them or rescans far too much. What helped was keying reconciliation off what actually changed upstream instead of a date window. Curious whether IFCO's late events had a hard upper bound on lateness, or if the window was picked from observed data.