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Early in my career, I got an IAM error. AccessDenied: User is not authorized to perform: s3:GetObject I panicked. Added "Action": "*" to the policy. It worked. That single move is one of the most common — and dangerous — mistakes data engineers make. IAM isn’t “someone else’s job.” Every Glue job, every Lambda function, every Redshift cluster — needs permissions to talk to other AWS services. If you’re writing pipelines, you’re writing IAM policies. Whether you realize it or not. 5 things every data engineer must know about IAM: 1. Users vs Roles — know the difference 👤 Users → for people (you logging into AWS console) 🤖 Roles → for services (Glue, Lambda, Redshift assuming permissions) Your pipelines should never use a user’s credentials. Always roles. 2. Two policies, two jobs 📜 Trust policy → “WHO can assume this role?” 📜 Permission policy → “WHAT can this role do?” A Glue job’s role trusts glue.amazonaws.com AND has permissions to read S3 write to Glue Catalog. 3. Least privilege isn’t optional "Action": "s3:*" on "Resource": "*" = an open door. Scope it down: "Action": ["s3:GetObject", "s3:PutObject"] "Resource": "arn:aws:s3:::my-bucket/raw/*" 4. Never hardcode credentials No access keys in your PySpark scripts. No .env files committed to Git. Glue, Lambda, EC2 — all can assume roles automatically. Use that. 5. One role per job type Don’t create one giant “DataEngineerRole” used everywhere. A Glue ETL role ≠ a Lambda trigger role ≠ a Redshift COPY role. Separate roles = smaller blast radius if something goes wrong. That "Action": "*" shortcut I used early on? In a real production environment, that’s the kind of mistake that shows up in a security audit — and in an interview question. Full breakdown with real policy JSON common mistakes → medium.com/@mallinathnpatil1… #DataEngineering #AWS #IAM #CloudSecurity #Python #DataEngineer

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Anthropic say: Data is not software I agree, AI won't replace Data Professionals. Data is ambiguity, business context, and the different perspectives of the humans who depend on it. Blog: 🔗 claude.com/blog/how-anthropi… #data #dataengineer #ai #claudecode #anthropic #fable

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จบการจัดการสมัยใหม่และเทคโนโลยี เคว้งอยู่ปีนึง ไปสมัครงานเดินสายเน็ต ติดตั้งไวไฟ ลงวินโดว์ ทำอยู่สามปีไปเป็น IT Support อยู่ปีครึ่ง ออกมาทำฝ่ายสนับสนุนข้อมูลหลังบ้าน7-11 อัพเป็น Data Analyst อยู่หลายปี เบื่อทำรายงาน ย้านมาเป็นDataEngineer ตอนนี้ จบที่ ETL Developer เขียนโค้ด

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See you next week at booth 533 of Databricks Data AI Summit June 15-18 in San Francisco! As enterprises deploy more AI agents, the gap between development and production oversight grows wider. We'll be showcasing the AI Control Plane, purpose-built for: • Standardized telemetry that discovers and monitors all your agents • Continuous evaluation without external API costs • Enforceable guardrails across input, execution, and output • Enterprise-grade audit trails for compliance and oversight Stop by our booth to learn more! #AI #ArtificialIntelligence #ML #MachineLearning #MLOps #LLMs #LLMOps #GenAI #GenerativeAI #DataScience #DataScientist #DataEngineering #DataEngineer #CIO #ResponsibleAI #AIObservability #Guardrails #LLMGuardrails #AIAgent

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Data engineers are in high demand! Earning the Databricks Certified Data Engineer Associate certification can help you stand out. Schedule your exam through Kryterion.com & come & take the exam at our Ashburn testing center. #DataEngineer #DatabricksCertified

Upserting data in a warehouse is tricky. What happens when it fails partway through? Are you sure you're updating only the right rows? MERGE INTO puts all your insert/update/delete logic in one atomic statement. Full guide 👇 startdataengineering.com/pos… #spark #sql #dataengineer

🚀 Hiring Data Engineers! Looking for professionals with 5 years of experience in: ✔ Python ✔ PySpark ✔ Azure Databricks ✔ Azure Data Factory 📍 Pan India (Hybrid) 📝 Face-to-Face Interview 📧 careers@techmantraglobal.com #Hiring #DataEngineer #Python #Azure #TechMantra

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Behind every successful AI model, dashboard, and business insight, there's a Data Engineer making data accessible, reliable, and scalable. Building the future, one pipeline at a time. #DataEngineering #DataEngineer #BigData #DataPipeline #ETL #Azure #AWS #GCP #AI