Data Lake Architecture Diagram
Show raw, refined and curated zones of a data lake feeding analytics and ML.
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A data lake architecture diagram shows how raw data of any format is ingested into low-cost object storage and progressively refined. The central object store is surrounded by ingestion connectors, zoned layers for raw, refined and curated data, a catalog for metadata and discovery, a processing engine like Spark, and consumers including analytics, machine learning and lakehouse query engines.
Data engineers and ML platform teams use this diagram when designing scalable storage for structured and unstructured data on S3, ADLS or GCS. It clearly communicates a data lake or lakehouse design, the medallion zone strategy, and how raw inputs become governed datasets for analytics and model training.
Great for
- Big data platform design
- ML data infrastructure docs
- Cloud storage cost planning
- Medallion architecture proposals
- Data governance onboarding
Frequently asked questions
What is a data lake?+
A data lake is a centralized repository, usually on cloud object storage, that stores raw structured, semi-structured and unstructured data at scale until it is needed for analytics or machine learning.
What are the zones in a data lake?+
A common medallion design uses a raw (bronze) zone for unprocessed data, a refined (silver) zone for cleaned data, and a curated (gold) zone for business-ready datasets.
What is the difference between a data lake and a lakehouse?+
A lakehouse adds warehouse-like features such as ACID transactions, schema enforcement and SQL performance on top of data lake storage using formats like Delta Lake or Iceberg.
Why do you need a data catalog?+
A catalog stores metadata and lineage so users can discover, understand and trust datasets, preventing the lake from becoming an unusable data swamp.
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