Data Engineering. ETL. Big Data. Cloud Pipelines.
Welcome to DATA-KING — the Data Engineering version of “Backend-King”.
This repository is not about APIs, microservices, or backend frameworks.
This is about data pipelines, transformations, analytics platforms, and cloud-scale ETL systems.
Think of this as a hands-on lab for mastering modern Data Engineering using Python, Spark, Snowflake, Databricks, Alteryx, Talend, and all major cloud providers.
DATA-KING is a collection of real-world examples for:
- Building batch & streaming pipelines
- Performing large-scale transformations
- Working with data warehouses and lakehouses
- Using cloud-native ETL tools
- Designing production-grade data architectures
It’s designed for:
- Data Engineers
- Analytics Engineers
- Cloud Data Engineers
- ML Engineers who want strong data foundations
| Area | Tools |
|---|---|
| Programming | Python, PySpark |
| Big Data | Apache Spark |
| Data Warehousing | Snowflake |
| Lakehouse | Databricks + Delta Lake |
| Cloud ETL | AWS Glue, Azure Data Factory, GCP Dataflow |
| Low-Code ETL | Alteryx, Talend |
| Orchestration | Airflow / Prefect |
| Storage | S3, GCS, Azure Blob |
| Compute | EMR, Dataproc, Synapse |
Typical pipeline flow: