---
title: "ETL"
description: "2 posts about ETL, summarised, each linking to the original."
---

# ETL
> 2 posts about ETL, summarised, each linking to the original.

## Articles

### [How to Repoint dbt ETL Pipelines to Databricks](https://yomu.fyi/post/how-to-repoint-dbt-etl-pipelines-to-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Khagay Nagdimov, Ismail Makhlouf, Shweta Verma
- Published: Oct 7, 2026
- Type: Tutorial

Repointing a dbt project to Databricks involves swapping the adapter and profile, handling SQL dialect differences, and keeping transformation logic unchanged. The dbt-databricks adapter bridges the project to Databricks Lakehouse, which uses a three-level namespace (catalog.schema.table) and supports open formats like Delta Lake and Apache Iceberg. Common dialect gaps include REGEXP\_SUBSTR, DIV0, and OBJECT\_CONSTRUCT, which can be fixed with Databricks-native functions or dbt\_utils macros; the post walks through each error and its fix. After compilation and validation using audit-helper to compare outputs model by model, the project can be deployed as a dbt task in Lakeflow Jobs with built-in governance via Unity Catalog. The process is incremental: one-time setup costs apply, then each additional model requires only dialect fixes, making it a low-risk starting point for warehouse migration.


### [What is Data Transformation?](https://yomu.fyi/post/what-is-data-transformation.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 3, 2026
- Type: Explainer

Data transformation turns raw data extracted from source systems into usable, trusted datasets for operational needs, analytics and decision-making. The process addresses inconsistencies, missing values and duplicates through techniques such as cleansing, de-duplication, integration, aggregation, validation and format revision, often mapping data to a target schema during ETL. Performance considerations include compaction, partitioning, file-size tuning, data skipping and pruning, and caching to reduce storage, scanning, processing time and latency. The article also compares on-premises, cloud, open-source, proprietary, no-code, low-code and codable ETL tools, then describes Spark Declarative Pipelines and Lakeflow Jobs on Databricks for quality checks, pipeline development, monitoring and orchestration. A Navy Federal Credit Union proof of concept illustrates a rapid deployment, with a proof of concept completed in a week and a dashboard released days after production deployment.