🚀 Roadmap to Master Data Engineering in 60 Days! 🛠️📊

📅 Week 1–2: Foundations
🔹 Day 1–3: Understand what Data Engineering is
🔹 Day 4–7: Learn SQL (joins, aggregations, subqueries)
🔹 Day 8–10: Learn Python for data (Pandas, basic scripts)
🔹 Day 11–14: Databases – RDBMS vs NoSQL (PostgreSQL, MongoDB)

📅 Week 3–4: Data Pipelines Storage
🔹 Day 15–18: ETL vs ELT concepts
🔹 Day 19–21: File formats – CSV, JSON, Parquet, Avro
🔹 Day 22–25: Data Warehousing – Snowflake, BigQuery, Redshift
🔹 Day 26–28: Batch vs Stream processing

📅 Week 5–6: Tools Frameworks
🔹 Day 29–33: Apache Airflow – scheduling, DAGs
🔹 Day 34–36: Apache Spark – basics, PySpark
🔹 Day 37–39: Kafka – streaming, producers/consumers
🔹 Day 40–42: Data Modeling – Star Snowflake schemas

📅 Week 7–8: Cloud, Projects Practice
🔹 Day 43–45: Learn basics of AWS/GCP/Azure (S3, EC2, BigQuery)
🔹 Day 46–50: Build a mini project (e.g. ETL pipeline with Airflow + Spark + S3)
🔹 Day 51–55: Data quality, testing, monitoring tools
🔹 Day 56–60: Mock interviews system design for data pipelines

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