Data Warehouse and Data Lake: Concepts, Architecture, and Comparison

The article begins by describing the rapid growth of data generated by internet and IoT technologies, introducing key concepts such as decision support systems (DSS), business intelligence (BI), data warehouses, data lakes, and data middle platforms, and explains why these terms are often confused.

It then details the differences between operational (transactional) databases and analytical databases, highlighting distinctions in data volume, latency, update patterns, and usage scenarios, and explains why separating these workloads is necessary.

Subsequent sections define OLTP and OLAP, discuss the evolution of data warehouse architectures, and introduce the concept of data lakes, including definitions from Wikipedia, AWS, and Microsoft, as well as their characteristics such as storing raw data of any type, supporting multiple processing models, and providing extensive data governance capabilities.

The article compares data warehouses and data lakes, outlining their storage scope, data types, flexibility, and suitability for different user groups, and presents a detailed comparison of their architectures, including storage, processing, and governance layers.

It also reviews major vendor solutions (AWS, Huawei, Alibaba Cloud, Azure) for building data lakes, describing components like data ingestion, metadata catalogs, ETL, compute engines, security, and governance, and evaluates each solution’s strengths and gaps.

Practical use cases are provided, such as advertising data analysis and game operation analytics, demonstrating how data lake architectures can improve scalability, cost efficiency, and analytical performance compared to traditional data warehouses.

Finally, the article discusses the future direction of data lake solutions, emphasizing cloud‑native design, robust data management, SQL‑based access, integration with data warehouses (Lakehouse), and the importance of aligning data platforms with business needs.