| Aspect |
Data Lake |
Data Warehouse |
| Definition |
A centralized repository that stores raw, unprocessed data in its
native format (structured, semi-structured, or unstructured).
|
A relational database optimized for querying and analysis of
structured data.
|
| Design Purpose |
Designed to handle large volumes of data from various sources,
including IoT devices, social media, and log files.
|
Designed to store processed and transformed data, typically from
operational systems and applications.
|
| Schema |
Schema-on-read, meaning the schema is defined when the data is
queried.
|
Schema-on-write, meaning the schema is defined when the data is
ingested.
|
| Validation |
Data is validated against the inferred schema during the read process,
ensuring consistency and integrity.
|
Data is validated against the schema during the write process,
ensuring consistency and integrity.
|
| Optimization |
Optimized for big data analytics, machine learning, and data science
use cases.
|
Optimized for business intelligence (BI), data visualization, and
reporting use cases.
|
| Scalability |
Flexible and scalable, allowing for easy addition of new data sources
and formats.
|
Typically used for strategic decision-making, forecasting, and
historical analysis.
|
| Examples |
Amazon S3, Azure Data Lake Storage, Hadoop Distributed File System
(HDFS).
|
Oracle, Microsoft SQL Server, IBM DB2.
|