A complete guide to cloud modern data architecture

This post details the core principles and implementation of modern data architecture, focusing on cloud-native approaches. It elaborates on batch and real-time data ingestion, data storage (data lakes, lakehouses, and storage zones), data catalogs, data transformation (SQL-based tools like dbt, Spark, Python, and stream processing engines), and security & governance. It outlines key implementation steps including establishing unified data storage, integrating real-time event pipelines (Kafka/Flink), enforcing federated governance, adopting cloud-native principles (separation of storage and compute), and incorporating AI for data quality and governance. The post also discusses bringing CI/CD to data products (DataOps) and the role of modern architecture in advanced analytics.