A data lake in AI is a large, centralized storage system where all types of data—structured and unstructured—are kept in their raw form. It’s essential for AI because:
- Data Variety: It stores different data types like text, images, and videos, which are needed to train AI models.
- Scalability: It handles huge amounts of data, crucial for AI’s big data needs.
- Flexibility: Data scientists can easily access and work with this data to develop AI models.
- Cost-Effective: It provides a cheaper way to store large volumes of data over time.
Examples of Data Lake products: Amazon S3 (Simple Storage Service), Microsoft Azure Data Lake, Google Cloud Storage, Apache Hadoop, Databricks Lakehouse.
Difference Between a Data Lake and Data Warehouse:
Data warehouse: similar to Data Lake but stores structured, organized data.
Examples of Data warehouse products: Amazon Redshift, Google Big Query, Microsoft Azure Synapse Analytics, Snowflake, IBM Db2 Warehouse.