--- title: 4 YAML Files Instead of PySpark: How We Let Analysts... description: A data engineering team at Mindbox replaced PySpark pipelines with a declarative stack using dlt, dbt, Trino, and Airflow+Cosmos, enabling analysts and product... canonical: https://daily.dev/posts/4-yaml-files-instead-of-pyspark-how-we-let-analysts-build-data-pipelines-without-engineers-kumvd7a9c twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers | daily.dev og:description: A data engineering team at Mindbox replaced PySpark pipelines with a declarative stack using dlt, dbt, Trino, and Airflow+Cosmos, enabling analysts and product... og:url: https://daily.dev/posts/4-yaml-files-instead-of-pyspark-how-we-let-analysts-build-data-pipelines-without-engineers-kumvd7a9c og:image: https://api.daily.dev/og/posts/KUMVd7a9c.png og:image:alt: 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers **[Towards Data Science](https://daily.dev/sources/tds)** · 11 min read · 0 upvotes · 0 comments ## Summary A data engineering team at Mindbox replaced PySpark pipelines with a declarative stack using dlt, dbt, Trino, and Airflow+Cosmos, enabling analysts and product managers to build data pipelines using only YAML and SQL. The approach reduces delivery time from weeks to one day. The post walks through four YAML/config files: a dlt.yaml for data ingestion, dbt SQL models with sources.yaml and dbt_project.yaml for transformations, and a dag.yaml for Airflow orchestration. Limitations are also covered, including dlt's experimental Delta upsert support, Trino's fault tolerance constraints at terabyte scale, and the difficulty of custom UDFs outside of SQL. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Your Data Pipeline Shouldn’t Require a PhD in YAML](https://daily.dev/posts/your-data-pipeline-shouldn-t-require-a-phd-in-yaml-te9wqbvsy) · Snowflake Community · 0 upvotes · 0 comments - [From Chaos to Scale: Templatizing Spark Declarative Pipelines with DLT-META](https://daily.dev/posts/from-chaos-to-scale-templatizing-spark-declarative-pipelines-with-dlt-meta-ikxbvtsvw) · databricks · 0 upvotes · 0 comments - [Build data pipelines in less time with Data Agent Kit](https://daily.dev/posts/build-data-pipelines-in-less-time-with-data-agent-kit-k8cquzavs) · Google Cloud · 1 upvotes · 0 comments - [Productionize Python ETL Scripts: Migrate to dlt & dltHub](https://daily.dev/posts/productionize-python-etl-scripts-migrate-to-dlt-dlthub-5ea79r3hn) · dltHub · 2 upvotes · 0 comments - [Snowflake Cortex Code CLI adds dbt and Apache Airflow support for AI-powered data pipelines](https://daily.dev/posts/snowflake-cortex-code-cli-adds-dbt-and-apache-airflow-support-for-ai-powered-data-pipelines-yrixxu8v0) · The New Stack · 1 upvotes · 0 comments --- Tags: [#backend](https://daily.dev/tags/backend), [#apache-airflow](https://daily.dev/tags/apache-airflow), [#pyspark](https://daily.dev/tags/pyspark) [View this post on daily.dev](https://daily.dev/posts/4-yaml-files-instead-of-pyspark-how-we-let-analysts-build-data-pipelines-without-engineers-kumvd7a9c) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers","url":"https://daily.dev/posts/4-yaml-files-instead-of-pyspark-how-we-let-analysts-build-data-pipelines-without-engineers-kumvd7a9c","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/4-yaml-files-instead-of-pyspark-how-we-let-analysts-build-data-pipelines-without-engineers-kumvd7a9c"},"datePublished":"2026-04-29T16:34:27.202Z","dateModified":"2026-04-29T16:59:53.737Z","description":"A data engineering team at Mindbox replaced PySpark pipelines with a declarative stack using dlt, dbt, Trino, and Airflow+Cosmos, enabling analysts and product...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/872ec717ef1969ab6e8836d835130f9c?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/872ec717ef1969ab6e8836d835130f9c?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/4-yaml-files-instead-of-pyspark-how-we-let-analysts-build-data-pipelines-without-engineers-kumvd7a9c","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"backend,apache-airflow,pyspark","timeRequired":"PT11M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers"}]} ```