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A Spanish data engineering roadmap sorts free resources by beginner, intermediate and advanced levels; start with its quick navigation index to jump to Programming or Databases.

Polars 2.0 mainly changed how existing code behaves. Polars 2.1 brings a more exciting set of new features. Here are three I found most useful: • Run group-bys faster with less memory • Use more warehouse-style SQL functions • Encrypt selected columns in Parquet files Polars 2.1 also adds support for conditional joins, approximate quantiles, and masked NumPy arrays. This article demonstrates each feature with concrete examples, outputs, and diagrams. 🚀 Full article: #DataEngineering #Python #DataScience #BigData

💥 A contrast between a data scientist, engineer, and analyst! Source: @BiDataTips #DataScience #DataAnalysts #DataEngineers

Learn why every challenger to SQL, from NoSQL and graph databases to vector databases and stream processing, eventually gets absorbed back into it, as a JanusGraph creator explains. #DataScience #AI #ArtificialIntelligence #ODSC #SQL #VectorDatabases #DataEngineering #StreamProcessing

The web scraping industry is evolving, and the conversation is no longer just about extracting data at scale. #WebScraping #ProxyInfrastructure #Automation #DataEngineering #wismartech #ArtificialIntelligence #founders #StartUps

Web Scraping Challenges in 2026: Infrastructure Risks, Data Quality and Compliance #WebScraping #ProxyInfrastructure #DataEngineering #DataPrivacy #Automation

Apache Iceberg v3 and V4 #DataEngineering #ApacheIceberg #DataLakehouse

Day 201 - SQL Advanced Only senior engineers get this right consistently. What does LATERAL join enable? Drop your answer (A/B/C/D) in the comments! Correct answers get a heart. Save this for your next interview prep session. #sql #database #dataengineering #sqlquery

Data & ML pipelines, from prototype to production. 📊 Robust pipelines that collect, clean and serve your data to models at production scale. #TheAIPlusPlus #MLOps #DataEngineering #MachineLearning

573 - One plan can't deploy into more than one workspace in a single run. There are limits on file size and group counts too. Worth reading the docs page before you get excited. #microsoftfabric #deployment #preview #dataengineering

⚡ Apache CarbonData Interview Questions & Answers Level up your Big Data skills and ace your Spark or Hadoop interviews with essential CarbonData insights 🚀 👉 Read here: #ApacheCarbonData #BigData #ApacheSpark #Hadoop #DataEngineering #Coding #TechTip

AI and data engineering are so similar it's painful! - Agent workflows = data pipelines - Chunking text = data modeling - Embeddings = dimensionality reduction - Retrieval = indexing and partitioning - Vector search = join optimization - Prompt engineering = query tuning - Guardrails = data quality checks If you don't see how these parallels mean data engineers are going to take over the world, you're blind! My Nov 16th boot camp covers both sides using Snowflake, Cortex and Claude. DM me for details!

Download this free book #datalakehouse #dataengineering #apacheiceberg

SQL queries don’t execute in the same order they’re written. Understanding how FROM, WHERE, GROUP BY, HAVING, SELECT, and ORDER BY work helps you write better queries, debug errors, and optimize performance. #SQL #DataAnalytics #DataScience #SQLTips #Database #DataEngineering #DataAnalyst

573 - Engineers touch dev. Only the release engineer touches test and production. That split has been the advice to clients for years, and deployment plans make it far easier to hold. #microsoftfabric #devops #deployment #dataengineering

575 - Classify IPv6 prefixes from T-SQL. That's the demo example they picked, after showing PDFs and emails. The real story got buried under a networking use case nobody asked for. #fabric #tsql #powerbi #dataengineering

Amazon S3 Tables now supports Hive-to-Iceberg migrations for Glue-registered Parquet tables within one AWS account and region. You can sync changes before cutover. AWS says source writes need a brief pause for that final step. #AWS #DataEngineering

𝗬𝗼𝘂𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗻𝗲𝗲𝗱𝘀 𝗱𝗮𝘁𝗮. 𝗬𝗼𝘂𝗿 𝘀𝗮𝗹𝗲𝘀 𝘁𝗲𝗮𝗺 𝗻𝗲𝗲𝗱𝘀 𝗶𝘁 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲𝗶𝗿 𝗖𝗥𝗠. Understanding that difference makes ETL, ELT, and Reverse ETL easier to explain. Consider an online retailer. 𝗟𝗲𝘁’𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 𝗶𝘁𝘀 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗱𝗮𝘁𝗮. 𝟭. 𝗘𝗧𝗟: 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 → 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 → 𝗟𝗼𝗮𝗱 Extract records from the order system. Clean them, standardize fields, and apply business rules before loading the target warehouse. → Useful when data needs transformation before entering the destination. 𝟮. 𝗘𝗟𝗧: 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 → 𝗟𝗼𝗮𝗱 → 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 Load source records into the analytical platform first. Then use its compute to build customer, order, and revenue models. → Useful when the destination can handle the transformations and teams need flexibility to remodel retained source data. 𝟯. 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗘𝗧𝗟: 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗱𝗮𝘁𝗮 → 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗼𝗼𝗹𝘀 Your warehouse identifies customers who have stopped purchasing. Sync that customer segment into the CRM so the sales team can act on it. → Useful when business teams need curated data inside their everyday applications. 𝗧𝗵𝗲 𝗸𝗲𝘆 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻: ETL vs. ELT describes when transformation happens relative to loading. Reverse ETL describes moving analytical data back into operational systems. All three can exist in the same architecture. In your next interview, go beyond expanding the abbreviations. Explain: → Where the data starts → Where transformation happens → Who uses the result → Why that flow fits the requirement Save this for your next Data Engineering interview. Top DE interview questions & answers for 2026 - #DataEngineering #ETL #ELT #DataArchitecture #InterviewPreparation

If you could master only ONE tech skill in 2027, what would you choose? A) AI Engineering B) Cloud Computing C) Data Engineering D) Cybersecurity