Post by Sunjana Ramana

If I were starting data engineering today, I would learn how pipelines work first. (Save this!) Because every real pipeline has the same basics: ↳ bring data in ↳ clean and transform it ↳ handle failures ↳ make it reliable ↳ make it fast ↳ keep it secure This Data Pipeline Patterns map helps with that. It shows: ↳ batch, streaming, and hybrid pipelines ↳ common architectures like Lambda, Kappa, and Medallion ↳ ingestion from files, APIs, CDC, logs, and webhooks ↳ ETL and ELT ↳ retries, deduplication, schema checks, and backfills ↳ partitioning, compaction, and scaling ↳ access control, audit, and retention It also connects these ideas to real tools. 𝐔𝐬𝐞𝐟𝐮𝐥 𝐭𝐨𝐨𝐥𝐬: ↳ Airflow: https://lnkd.in/dVKBgZak ↳ Kafka: https://lnkd.in/eA_KXNvN ↳ dbt: https://lnkd.in/e7Mtuemp ↳ Spark: https://lnkd.in/ebtAKz7g ↳ Structured Streaming: https://lnkd.in/dAwVYqtC This is how I would start. Learn the patterns. Then learn the tools. ♻️ Repost this to help someone learning data engineering 📌 Build a Data Engineering portfolio in just 3 weeks 👉 https://datadrooler.com/