--- title: Best Apache Spark posts of 2025 | daily.dev description: The most upvoted Apache Spark posts of 2025, curated by the daily.dev community. canonical: https://daily.dev/tags/apache-spark/best-of/2025 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/tags/apache-spark/best-of/2025 og:type: website og:site_name: daily.dev og:title: Best Apache Spark posts of 2025 | daily.dev og:description: The most upvoted Apache Spark posts of 2025, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Apache Spark — 2025 1. 1 [](/posts/data-pipelines-in-machine-learning-systems--spgcmlhiu "Data Pipelines in Machine Learning Systems.") Article SwirlAI · 2y Data Pipelines in Machine Learning Systems. This tutorial guides through implementing a real-time data ingestion pipeline for machine learning systems using FastAPI and Apache Spark. Key steps include writing a FastAPI collector application, downloading and pushing data from the internet to this application, and processing the data via a Spark ETL pipeline managed by Airflow, all deployed on the Nebius AI Cloud platform. The tutorial emphasizes ensuring data quality and integrity at each stage and showcases setting up Kubernetes clusters for high availability and managed data operations. 138 2. 2 [](/posts/data-engineer-project-from-streaming-orders-to-batch-insights-a-coffee-shop-chain-s-data-pipeline-n17ghbzoj "Data Engineer Project: From Streaming Orders to Batch Insights — A Coffee Shop Chain’s Data Pipeline") Article Data Engineering · 1y Data Engineer Project: From Streaming Orders to Batch Insights — A Coffee Shop Chain’s Data Pipeline A comprehensive data engineering project demonstrates building a complete pipeline for a coffee shop chain that processes real-time orders and provides instant product recommendations while supporting batch analytics. The implementation uses modern tools including Kafka for streaming, Spark for processing, Airflow for orchestration, Delta Lake for storage, Redis for caching, and MinIO for object storage. The project showcases Lakehouse architecture, data quality validation, and SCD Type 2 dimension modeling with full documentation and production-ready simulation. 97 2 3. 3 [](/posts/decathlon-switches-to-polars-to-optimize-data-pipelines-and-infrastructure-costs-5gcd7b1tn "Decathlon Switches to Polars to Optimize Data Pipelines and Infrastructure Costs") Article InfoQ · 41w Decathlon Switches to Polars to Optimize Data Pipelines and Infrastructure Costs Decathlon migrated data pipelines processing small to mid-size datasets (under 50 GiB) from Apache Spark clusters to Polars running on single Kubernetes pods. The switch reduced compute launch time from 8 to 2 minutes and significantly lowered infrastructure costs. Polars' streaming engine enables processing datasets larger than available memory on modest hardware. The team now uses Polars for new pipelines with stable, smaller input tables that don't require complex joins or aggregations, while keeping Spark for terabyte-scale workloads. Challenges include managing Kubernetes infrastructure and limitations with certain Delta Lake features. 70 4. 4 [](/posts/the-ultimate-roadmap-to-become-a-data-engineer-in-2025-with-free-resources--cbcld4cht "The Ultimate Roadmap to Become a Data Engineer in 2025 (With Free Resources)") Article Data Engineer Things · 1y The Ultimate Roadmap to Become a Data Engineer in 2025 (With Free Resources) A comprehensive guide for becoming a data engineer in 2025, covering essential skills like SQL, Python, and data modeling, along with big data technologies like Apache Spark and cloud platforms. The roadmap emphasizes free learning resources and practical experience, highlighting that mastering core principles enables quick adaptation to new tools in the rapidly evolving data engineering landscape. 46 2 5. 5 [](/posts/introducing-apache-spark-4-0-psljz0xce "Introducing Apache Spark 4.0") Article databricks · 1y Introducing Apache Spark 4.0 Apache Spark 4.0 introduces key advancements in SQL language, Python support, structured streaming, and usability, enhancing big data processing. Notable features include improved multi-language compatibility, new SQL scripting capabilities, enhanced Python APIs, and structured logging. This release offers greater modularity, scalability, and standards compliance, making it future-ready for large-scale data analytics. 43 6. 6 [](/posts/apache-iceberg-1-10-maturing-the-v3-spec-the-rest-api-and-google-contributions-ajhdxw5lb "Apache Iceberg 1.10: Maturing the V3 spec, the REST API and Google contributions") Article Google Open Source Blog · 1y Apache Iceberg 1.10: Maturing the V3 spec, the REST API and Google contributions Apache Iceberg 1.10.0 introduces major improvements including full Spark 4.0 and Flink 2.0 compatibility, production-ready Deletion Vectors for faster row-level updates, and a hardened REST Catalog API. The release matures the V3 specification with features like row lineage and variant types. Google contributed native BigQuery Metastore Catalog support and Google AuthManager, enabling seamless integration with BigLake-managed tables through open REST protocols. 16 7. 7 [](/posts/building-engine-agnostic-data-stacks-skguunpbh "Building Engine-Agnostic Data Stacks") Article dltHub · 1y Building Engine-Agnostic Data Stacks Modern data teams often use multiple engines like Spark, DuckDB, and Snowflake, but struggle with data portability and code reusability across platforms. Apache Iceberg solves the storage problem by enabling safe data sharing between engines through ACID transactions and multi-engine coordination. Tools like Ibis complement this by providing engine-agnostic analytical code that runs on any supported backend without modification. Together, these technologies create truly portable data stacks where both data and business logic are decoupled from specific compute engines, reducing vendor lock-in and integration overhead. 16 8. 8 [](/posts/diving-into-data-lakehouse-databricks-101-5r6ijuz8f "Diving into Data LakeHouse: Databricks 101") Article Data Engineer Things · 2y Diving into Data LakeHouse: Databricks 101 Databricks offers an advanced Data Lakehouse solution, integrating the capabilities of Data Lakes and Data Warehouses. Built on Apache Spark, Databricks enables efficient data processing, data analysis, machine learning, and business intelligence. The platform leverages Delta Lake for reliable and consistent data storage, along with Unity Catalog for centralized access control and data management. 13 9. 9 [](/posts/handle-schema-evolution-like-your-job-depends-on-it-qrzob6gow "Handle Schema Evolution like your job depends on it") Article Towards Dev · 1y Handle Schema Evolution like your job depends on it Schema evolution in data engineering involves handling structural changes to incoming data without breaking existing pipelines. The solution involves maintaining a schema evolution master table for tracking changes, handling all schema evolution at the bronze layer, using target schemas for crucial columns, and locking schemas in silver and gold layers. A practical implementation includes an align\_schema function that adds missing columns as nulls, drops extra columns, and logs all schema changes to a Delta table for monitoring and proof of schema modifications. 10 [See all Apache Spark archives](/tags/apache-spark/best-of) ```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","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/tags/apache-spark/best-of/2025#page","url":"https://daily.dev/tags/apache-spark/best-of/2025","name":"Best Apache Spark Posts of 2025","description":"The most upvoted Apache Spark posts of 2025, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/tags/apache-spark/best-of/2025#items","numberOfItems":9,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/data-pipelines-in-machine-learning-systems--spgcmlhiu","name":"Data Pipelines in Machine Learning Systems."},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/data-engineer-project-from-streaming-orders-to-batch-insights-a-coffee-shop-chain-s-data-pipeline-n17ghbzoj","name":"Data Engineer Project: From Streaming Orders to Batch Insights — A Coffee Shop Chain’s Data Pipeline"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/decathlon-switches-to-polars-to-optimize-data-pipelines-and-infrastructure-costs-5gcd7b1tn","name":"Decathlon Switches to Polars to Optimize Data Pipelines and Infrastructure Costs"},{"@type":"ListItem","position":4,"url":"https://daily.dev/posts/the-ultimate-roadmap-to-become-a-data-engineer-in-2025-with-free-resources--cbcld4cht","name":"The Ultimate Roadmap to Become a Data Engineer in 2025 (With Free Resources)"},{"@type":"ListItem","position":5,"url":"https://daily.dev/posts/introducing-apache-spark-4-0-psljz0xce","name":"Introducing Apache Spark 4.0"},{"@type":"ListItem","position":6,"url":"https://daily.dev/posts/apache-iceberg-1-10-maturing-the-v3-spec-the-rest-api-and-google-contributions-ajhdxw5lb","name":"Apache Iceberg 1.10: Maturing the V3 spec, the REST API and Google contributions"},{"@type":"ListItem","position":7,"url":"https://daily.dev/posts/building-engine-agnostic-data-stacks-skguunpbh","name":"Building Engine-Agnostic Data Stacks"},{"@type":"ListItem","position":8,"url":"https://daily.dev/posts/diving-into-data-lakehouse-databricks-101-5r6ijuz8f","name":"Diving into Data LakeHouse: Databricks 101"},{"@type":"ListItem","position":9,"url":"https://daily.dev/posts/handle-schema-evolution-like-your-job-depends-on-it-qrzob6gow","name":"Handle Schema Evolution like your job depends on it"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Tags","item":"https://daily.dev/tags"},{"@type":"ListItem","position":3,"name":"Apache Spark","item":"https://daily.dev/tags/apache-spark"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/tags/apache-spark/best-of"},{"@type":"ListItem","position":5,"name":"2025"}]}]} ```