# Data Mesh vs Lake vs Warehouse --- - They are all concepts in the realm of data architecture and data management - Understood as both approaches and concepts depending on context --- # Data Mesh  --- # Data Mesh - Tech Stacks - Data storage: Amazon S3, Google Cloud Storage, Azure Blob Storage. - Data processing: Apache Spark - Data governance: Collibra - Data catalog: Atlan - Data virtualization: Denodo - Data mesh orchestration: Meshery --- # Data Lake --- - a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. - often used to store raw data that can be processed and analyzed later. - can be very scalable and cost-effective - can also be difficult to manage and search. (source: cloud.google.com) --- # Data Lake - Tech Stacks - Storage: Amazon S3, Hadoop - Processing: Apache Spark - Governance: Collibra - Visualization: Tableau --- # Data Warehouse  --- - a central repository of structured information that can be analyzed to make more informed decisions - typically designed for business intelligence or reporting - can be expensive to implement and maintain (source: aws.amazon.com) --- # Data Warehouse - Tech Stacks - Database: Amazon Redshift - Data pipeline: Airflow - Data modeling: ERwin Data Modeler - Data governance: Collibra - BI tools: Tableau --- ### Summary  --- # Thank you! Sources: - https://www.datamesh-architecture.com/ - https://cloud.google.com/learn/what-is-a-data-lake - https://aws.amazon.com/data-warehouse - Bard - ChatGPT ---