A data lake is a centralized hub that stores raw data from various sources in its original format, whereas a data mesh divides stored data across business areas, promoting domain ownership. Data lakes are ideal for companies with unstructured data coming from multiple sources and limited financial resources to invest in new technology. They offer flexibility, scalability, and real-time data ingestion and analysis capabilities. However, they can lead to data quality issues and require centralized governance. On the other hand, a data mesh promotes decentralized ownership, agility, and responsiveness, but it requires skilled teams with domain expertise and modern cloud infrastructure. It is suitable for organizations that deal with various data types and sources, seek agility and responsiveness, and have the capacity to invest in new technologies and training. To maximize the potential of either approach, organizations need a framework to monitor the health, accuracy, and reliability of their data pipeline.