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tina-cloud-app[bot] authored Jan 19, 2024
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---
title: Data Engineering as a Product
date: '2024-01-15'
author: 'Chris Forno'
author: Chris Forno
title: Data Engineering as a Product
---

Data engineering is essential for all value-added data work, but it’s hard to do well and ripe for disruption.
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According to various surveys, “data engineering” (collection, copying, cleaning, etc.) is the most hated part of data scientists’ jobs. Data engineering consists of:

* Copying data from one place to another (often with [ETL pipelines](dont-etl-elt-or-etlt))
* Converting data between different formats (such as [CSV](why-you-need-automatic-inference))
* Aligning data from different sources
* Copying data from one place to another (often with [ETL pipelines](dont-etl-elt-or-etlt)).
* Converting data between different formats (such as [CSV](why-you-need-automatic-inference)).
* Aligning data from different sources.

These are all repetitive, error-prone tasks: exactly the kinds of tasks that are well-suited to automation.

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