Data Lake
A storage repository holding vast amounts of raw data in native format until needed. Unlike warehouses, lakes store unstructured and semi-structured data without predefined schemas.
In-Depth Explanation
A data lake stores data in its raw, native format - structured, semi-structured, and unstructured. Data is loaded as-is and transformed only when needed (schema-on-read).
Data lake characteristics:
- Raw storage: Data kept in original format
- Schema-on-read: Structure applied at query time
- Diverse data types: Structured, semi-structured, unstructured
- Massive scale: Petabytes of data cost-effectively
- Flexible: Support varied analytics use cases
Data lake use cases:
- Machine learning training data
- Log and event analysis
- IoT sensor data
- Document and media storage
- Data science exploration
Data lake platforms:
- AWS S3 + Athena/Glue
- Azure Data Lake Storage
- Google Cloud Storage
- Databricks Delta Lake
- Apache Hadoop/Spark
Business Context
Data lakes enable AI and advanced analytics by storing diverse data types cost-effectively. They complement warehouses for different use cases.
How Clever Ops Uses This
We design data lake architectures for Australian businesses, particularly for AI/ML workloads requiring diverse training data.
Example Use Case
"Storing raw customer interaction logs, images, documents, and IoT sensor data for future ML model training and exploratory analysis."