A data fabric is a unified architectural framework that integrates and connects disparate data sources, regardless of location, to enable sharing for analysis and reporting across departments. It offers improved data access, integration across platforms, data automation capabilities, and improved data quality by standardizing data integration and management practices. However, it also has limitations such as lack of maturity, high costs, and complex deployments. A data lake is a centralized repository designed to store vast amounts of raw data in its native format without pre-processing or transformation. It offers advantages like data ingestion flexibility, scalability, and the ability to handle diverse data types and sources. However, it also presents challenges such as lack of structure, data governance and security issues. The key differences between data fabrics and data lakes lie in their focus on storage versus management, data format, data governance and security capabilities, and data integration. When choosing between a data fabric or a data lake, organizations should consider factors like data volume and variety, business needs and data goals, technical expertise and data culture, and budget constraints.