Data Lake Architecture
Data Lake Architecture is a design pattern for storing vast amounts of raw, structured, semi-structured, and unstructured data in its native format at scale. It provides a centralized repository that allows organizations to store data from various sources without needing to structure it upfront, enabling flexible data exploration, analytics, and machine learning. The architecture typically includes components for data ingestion, storage, processing, governance, and consumption.
Developers should learn Data Lake Architecture when building systems that require handling diverse, high-volume data sources (e.g., IoT sensors, logs, social media feeds) for big data analytics, AI/ML model training, or real-time processing. It's particularly useful in scenarios where data schemas are unknown or evolving, as it avoids the rigidity of traditional data warehouses and supports cost-effective storage solutions like cloud object storage.