A data lake is a centralized repository that stores raw data in its original format, whether structured, semi-structured, or unstructured. Unlike data warehouses, data lakes do not require predefined schemas.

Examples of data stored in a data lake include:

• Application logs

• Clickstream events

• JSON API responses

• Images and videos

• Sensor and IoT data

Data lakes are commonly built on cloud object storage such as Amazon S3, Google Cloud Storage, or Azure Data Lake.

The key advantage of a data lake is flexibility. Data can be ingested quickly without upfront modeling. This is useful for exploratory analysis, data science, and machine learning.

However, data lakes introduce challenges:

• Poor data quality

• Lack of structure

• Discoverability issues

• Governance complexity

Without proper management, data lakes can turn into “data swamps.”

Modern architectures often combine data lakes with warehouses in a lakehouse model, blending flexibility with structure.

In BI, data lakes are typically used as staging areas or for advanced analytics rather than direct dashboarding.