Traditional vs Modern Data Architecture

1. Introduction In many companies, data comes from different systems like ERP, CRM, application databases, and web logs. This data is used for reports, dashboards, and business decisions. To use this data properly, we need a data architecture. There are two main types of data architecture: Traditional Data Architecture (ETL + Data Warehouse) Modern Data Architecture (ELT + Data Lake + Lakehouse) This document explains both approaches. It also explains why we use tools like Data Lake, Data Warehouse, Spark, Databricks, Delta Lake, Iceberg, Snowflake, BigQuery, Redshift, ADLS, GCS, S3, and Datadog. 2. High-Level Data Flow Data Sources → Ingestion → Data Lake → Processing → Lakehouse Tables → Data Warehouse → BI & Reports → Monitoring This means: Data comes from source systems. Data is ingested (copied) into the platform. Raw data is stored in a data lake. Data is cleaned and transformed using processing tools. Clean and reliable tables are created. Final data is loaded into a data wareho