Prakshi Yadav - Data lake: Design for schema evolution

Key Takeaways

• A data lake is a centralized repository that stores all types of data in its raw form, allowing for flexible schema on read and write.
• Schema evolution is a critical problem in a data lake, as data schema can change over time due to changes in data structure or data type.
• Avro provides a solution to schema evolution by allowing for backwards and forwards compatibility, and can store both the data and its schema in a single file.
• The key characteristics of a data lake include scalability, reliability, and fault tolerance.
• Storage classes should be designed to optimize storage and retrieval of data, and should include features such as data compression and encoding.
• Search and query should be easily accessible and easily searchable, with the ability to provide metadata and query results in real-time.
• Processing should be able to handle large volumes of data, and should be designed to handle parallel processing and distributed computing.
• Role-based access control should be implemented to ensure data security and integrity.
• Data should be encrypted both at rest and in transit to ensure data security and integrity.
• The storage solution should be designed to handle large volumes of data and should be scalable on demand.
• Data serialization should be designed to handle complex nested JSON data and should be able to handle data of various types.
• The schema registry should be designed to handle multiple versions of the schema and should provide a centralized location for storing and managing schema versions.