Data Engineering Weekly - The Weekly Data Engineering Newsletter

Data Engineering Weekly is a weekly aggregation of data engineering articles right into your inbox.

You will start receiving updates right here in your inbox. You can also log in to the website to read the full archives and other posts as they are published.


Editorial Policy

Purpose and Mission

Data Engineering Weekly exists to curate, highlight, and contextualize the most important developments in data engineering. Our mission is to provide practitioners, researchers, and enthusiasts with a trusted weekly digest that emphasizes quality, neutrality, and empathy.

We believe in vendor-neutral coverage, respectful commentary, and high editorial standards that reflect the evolving landscape of modern data engineering.


Content Selection and Curation

  • Rigorous Review Process: Each week, we review 15+ articles, papers, and blog posts before selecting only a handful to feature.

  • Quality over Quantity: Inclusion is based on clarity, originality, and relevance—not on popularity or volume.

  • Vendor Neutrality: Articles that serve primarily as promotional content for specific vendors, tools, or services will not be included.

  • Diversity of Perspectives: We aim to showcase voices from across the community—practitioners, researchers, and open-source contributors alike.


Submission Guidelines

We welcome suggestions and article submissions from the community, with the following principles:

  1. Relevance: Content must be directly related to data engineering and adjacent fields (data infrastructure, pipelines, orchestration, scalability, etc.).

  2. Neutrality: Submissions must avoid overt promotion of vendors, products, or paid services.

  3. Respectful Tone: Authors spend tremendous time and effort creating their work. Review snapshots will emphasize constructive takeaways and avoid negative or dismissive commentary.

  4. Editorial Discretion: The editorial team may accept or reject submissions without further explanation.

  5. Voluntary Contributions: Contributions are voluntary and not monetized. No financial compensation is provided for submissions.


Review Snapshots

When we summarize an article, our focus is to:

  • Highlight the main idea and contribution clearly.

  • Provide positive, empathetic commentary that adds context.

  • Encourage readers to explore the original work rather than replacing it.


Transparency and Integrity

  • Sponsored Content: Any sponsored or partnership content will be clearly labeled. Sponsored content does not influence article selection in the weekly digest.

  • Corrections: If factual errors are identified in summaries, we will correct them promptly in the next available issue or in an online update.

  • No Hidden Agendas: We are not affiliated with any single vendor or platform. Our goal is to reflect the most exciting developments in data engineering genuinely.


Community Conduct

Data Engineering Weekly is committed to fostering an inclusive, respectful community of readers and contributors. We ask everyone engaging with our content to:

  • Value constructive feedback over criticism.

  • Respect diverse perspectives and backgrounds.

  • Please help us keep the newsletter a welcoming resource for all data professionals.


Final Note

We publish Data Engineering Weekly because we are passionate about the field and its people. Our commitment is to serve the community first, curating content with fairness, empathy, and a high editorial bar.

Please check https://github.com/ananthdurai/dataengineeringweekly#how-can-i-contribute-articles

Interested in Writing an Op-Ed in Data Engineering Weekly?

The Data Engineering Weekly occasionally publishes op-eds about recent industry trends and observations. Please email me at ananth@dataengineeringweekly.com with your proposal.

Some of the most loved op-eds in recent times

Data Engineering Weekly
Introducing Schemata - A Decentralized Schema Modeling Framework For Modern Data Stack
I’m thrilled to write about Schemata, a decentralized schema modeling framework for data contracts. Oh, wait, all the jargon, what is it? Let me take you all on the Schemata journey. You can find the source code and the documentation here. GitHub Repo…
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Data Engineering Weekly
Bundling Vs. UnBundling: The Tale of Airflow Operator and dbt Ref
I started working on the data pipeline at the early stage of Hadoop/ Bigdata when Big Data was a buzzword. Apache Oozie (anyone remembers Oozie?) is a go-to tool to orchestrate the data pipeline, where you have to hand-code workflow in an XML file(not surprisingly, the file name is workflow.xml…
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Data Engineering Weekly
Functional Data Engineering - A Blueprint
The Rise of Data Modeling Data modeling has been one of the hot topics in Data LinkedIn. Hadoop put forward the schema-on-read strategy that leads to the disruption of data modeling techniques as we know until then. We went through a full cycle that…
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Data Engineering Weekly
Data Catalog - A Broken Promise
Data catalogs are the most expensive data integration systems you never intended to build. Data Catalog as a passive web portal to display metadata requires significant rethinking to adopt modern data workflow, not just adding “modern” in its prefix…
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Perfect — you already have a strong start with the GitHub draft. Let me expand it into a comprehensive, professional editorial policy that incorporates your points, while also strengthening trust signals for readers, contributors, and AI platforms like ChatGPT. You can add this as a dedicated page (e.g., /editorial-policy) on dataengineeringweekly.com.


About Us

Ananth Packkildurai is a freelance technology blogger. Data Engineering Weekly, owned and operated by Dewpeche India Private Ltd.

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Ananth Packkildurai is a data engineering leader, writer, and author of Data Engineering Weekly, sharing insights on modern data platforms, large-scale pipelines, and AI-driven architectures.