article Metadata-Driven ETL Framework in Databricks (Part-1) in Technical Blog

Metadata-Driven ETL Framework in Databricks (Part-1)

Rjt_de — Mon, 21 Oct 2024 08:09:27 GMT

In modern data-driven enterprises, data flows like lifeblood through complex systems and repositories to drive decision-making and innovation. Each dataset, whether structured or unstructured, holds the potential to unlock insights and drive innovation. However, transforming raw data into actionable intelligence is a complex challenge that demands a dynamic, adaptable framework capable of accelerating data ingestion and transformation.

In this three-part blog, we will cover the metadata setup (part 1), orchestration (part 2), and deployment process (part 3) of a configuration/metadata-driven ETL framework in Databricks following the Lakehouse architecture.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

camrojas — Thu, 24 Oct 2024 16:54:05 GMT

Excited to see the second part.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

ArunPrasanthS — Mon, 28 Oct 2024 07:55:11 GMT

Great stuff. Waiting for part 2

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

JesseS — Thu, 31 Oct 2024 11:30:16 GMT

I'm eagerly awating the next part. I do have some questions that I hope will get addressed, or maybe someone can share some insights on.

1. Are the framework tables stored as Delta tables in the lakehouse? I envision a seperate config schema that would house these tables, but I haven't worked with ingesting YAML files before so a bit confused on this point.

2. Will there be sample code showing a potential implementation of the generic execution notebooks? I am very much interested in how the audit tables get populated at run time with the statistics. We homebrewed an ETL monitoring system that relies on orchestration from Azure Data Factory, and I am hoping to be able to replace that with something more streamlined.

3. The article mentioned a separate pipeline to ingest the YAML files - is that planned for a future part of this series? I've tried searching the community forums but haven't been able to find much information on this, so I hope it is covered.

Excellent article, and I am excited to see what I can learn from the future parts so that I can apply them to my ETL environment.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

h_h_ak — Thu, 31 Oct 2024 13:22:05 GMT

Thanks, it looks great!

Do you have any code or repository examples to share? We implemented a similar approach, and we also added some metadata, such as PII table identifiers and additional table properties, to our configuration.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

AjayVerma — Tue, 05 Nov 2024 14:48:28 GMT

Nice article ... eagerly waiting for part-2

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

Mario_D — Fri, 15 Nov 2024 10:44:48 GMT

I'm just going to repeat what was said before, good article .......really, eagerly waiting for the follow up.

No pressure 😀

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

szymon_dybczak — Fri, 15 Nov 2024 16:38:37 GMT

Great article. Can't wait for second part! 🙂

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

ptambe — Thu, 09 Jan 2025 06:39:27 GMT

Nice article, we have built simlar frameworks. Waiting for the next part.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

warlord — Wed, 15 Jan 2025 08:50:58 GMT

I feel that the challenges will be far more due to the limitations of the workflow & Databricks. For example- How to install the libraries dynamically to the cluster etc.

And also this type of framework should not be called as ETL framework. Because it will not work for all the use cases.
At best you can separate it out and call it Orchestration Framework because you can not drive the transformations through the metadata. You can only orchestrate your notebooks through a framework (including few key details like table names and stuff.)

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

CuriousExplorer — Tue, 21 Jan 2025 01:36:25 GMT

Does it design relate to the implementation provided here: https://github.com/databrickslabs/dlt-meta?tab=readme-ov-file

Databricks team has already published this library dlt-meta . I am trying to understand how this design discussion correlates to what is in that repo ?

Can anyone provide any suggestions here ?

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

-werners- — Tue, 21 Jan 2025 10:28:11 GMT

Interesting,

but I start to get the feeling that this might be overengineered.
A framework like this is almost a classic DWH with a ton of control/system tables and saving/controlling state.
Coming from classic data warehousing, this is not where I want to end up again.

But that might just be me.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

BradSheridan — Tue, 28 Jan 2025 17:22:29 GMT

Really great article! Any timing on your Part 2 article??

Thanks!

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

WiseBrick — Wed, 29 Jan 2025 11:05:53 GMT

Indeed very promissing article. Why are the other two part of this blog?

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

jonsan — Wed, 29 Jan 2025 12:00:43 GMT

Great breakdown of the ETL framework design! The emphasis on modularity, scalability, and auditability is spot on for building a robust data pipeline. I'm excited to see how orchestration will enhance the architecture in part 2!

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

Mantsama4 — Mon, 03 Feb 2025 06:37:07 GMT

This is a fantastic post! The Metadata-Driven ETL Framework is a powerful approach to making data pipelines more scalable, efficient, and easier to manage within Databricks. The structured metadata-driven execution reduces manual intervention and enhances governance, which is critical for enterprise-scale ETL processes.

Few Questions:

Scalability and Performance: How does the framework address performance optimization, especially for large datasets and complex transformations? Are there specific techniques or tools used to ensure efficient processing?
Error Handling and Logging: What mechanisms are in place for error handling and logging within the framework? How are errors captured, reported, and addressed to ensure data integrity and pipeline resilience?
Data Lineage and Governance: How does the framework support data lineage tracking and governance processes? Can users easily trace the origin and transformations of data within the pipelines?
Integration with Databricks Features: How does the framework leverage Databricks features like Delta Live Tables (DLT) or Databricks SQL for data quality and transformation tasks?
Maintenance and Updates: How is the framework maintained and updated? Is there a process for incorporating new features, bug fixes, and enhancements?

I truly appreciate the depth of knowledge shared in this post and look forward to learning more about the real-world applications of this framework. Thanks for this insightful piece!

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

CARAQUE — Thu, 06 Feb 2025 01:14:34 GMT

Comment to back with the second part.

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

BradSheridan — Thu, 06 Feb 2025 15:12:03 GMT

@CuriousExplorer I too have the same exact question. Have you gotten any additional insight?

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

Flaviodiasps — Wed, 12 Feb 2025 20:04:41 GMT

It is a great article, I am excited for the next parts.

I am not sure about having these metadata tables in the lakehouse. It forces us to build a data pipeline for the metadata table. Isn't it better to just use a transactional database like mongo or postgres for it and let CICD pipeline fill them?

Re: Metadata-Driven ETL Framework in Databricks (Part-1)

WiseBrick — Fri, 28 Feb 2025 09:22:04 GMT

@DataBricks. When could we expect the next parts ?