Post by Gowtham SB

The Ultimate Data Engineer Cheat Sheet After working on pipelines, cloud migrations, SQL optimization, Spark jobs, and production systems, I realized something: You do NOT need to memorize everything. You just need a solid cheat sheet. Here’s a practical one 👇 ━━━━━━━━━━━━━━━━━━━━ 📌 SQL Essentials Joins: • INNER JOIN • LEFT JOIN • RIGHT JOIN • FULL JOIN • SELF JOIN Window Functions: • ROW_NUMBER() • RANK() • DENSE_RANK() • LAG() • LEAD() • NTILE() Aggregations: • COUNT() • SUM() • AVG() • MIN() • MAX() Advanced: • CTE (WITH) • Subqueries • CASE WHEN • UNION vs UNION ALL • CREATE TABLE AS (CTAS) • Temporary Tables • PARTITION BY • ORDER BY • HAVING ━━━━━━━━━━━━━━━━━━━━ 🐍 Python Essentials Data Structures: • List • Tuple • Dictionary • Set Must Know: • List Comprehensions • Lambda Functions • map() • filter() • zip() • enumerate() Performance: • Generators • Iterators • decorators • collections module • itertools module Libraries: • pandas • requests • json • datetime ━━━━━━━━━━━━━━━━━━━━ ⚡ PySpark Essentials DataFrame Operations: • select() • filter() • where() • withColumn() • drop() • distinct() Transformations: • groupBy() • agg() • join() • union() • explode() Optimization: • cache() • persist() • repartition() • coalesce() • broadcast join ━━━━━━━━━━━━━━━━━━━━ ☁️ Cloud Services AWS: • S3 • Glue • Athena • EMR • Lambda • Redshift GCP: • BigQuery • Dataproc • Dataflow • Pub/Sub • Cloud Storage Azure: • Data Factory • Synapse • Data Lake Storage • Event Hub ━━━━━━━━━━━━━━━━━━━━ 🔄 Data Pipeline Flow Data Source ↓ API / Database / Logs ↓ Ingestion Layer ↓ Storage Layer ↓ Transformation Layer ↓ Data Warehouse ↓ Dashboard / ML / Reporting ━━━━━━━━━━━━━━━━━━━━ 🔥 Linux Commands pwd → current path ls → list files cd → change directory grep → search text cat → read file head → first lines tail -f → live logs ps → running process top → system usage kill → stop process scp → transfer files chmod → permissions ssh → remote login ━━━━━━━━━━━━━━━━━━━━ 📦 Tools Every Data Engineer Sees • Airflow • Kafka • Hive • Snowflake • Docker • Kubernetes • dbt • Git • Jenkins ━━━━━━━━━━━━━━━━━━━━ 💡 Remember: Data Engineering is not: "Learn 100 tools" It is: Move data efficiently Store data correctly Process data reliably Build systems that scale Save this for interviews, projects, and daily work. What else belongs in this cheat sheet? #DataEngineering #SQL #Python #PySpark #BigData #AWS #GCP #Cloud #DataEngineer #Tech