This architecture allows for establishing standardized data structures and metadata as data. It may be ingested from diverse sources without strict controls, potentially leading to data inconsistency, duplication, and accessibility problems.ĭata governance in a data lakehouse is more structured due to the schema enforcement and indexing applied during data ingestion. Based on Apache Iceberg and Spark 3, their new stack helped them save more than 50% on compute resources and 40% on job elapsed time reduction in their data ingestion framework.ĭata governance can be challenging in a data lake due to the need for a more predefined structure and standardized metadata. If we take Airbnb, for example, to enhance their infrastructure, they upgraded to lighthouse architecture. This schema-on-read approach balances the flexibility of raw data storage and the performance optimization of structured data warehouses. This means that as data is ingested into the data lakehouse, some structure is imposed on the data to facilitate more efficient querying and analysis later on. While maintaining the raw format, an additional layer is added for schema enforcement and indexing during ingestion. A data lake’s primary focus is storing vast amounts of data with minimal upfront processing.ĭata ingestion is more refined in a data lakehouse than in a traditional one. This raw data is then stored within the data lake, often using distributed file systems or object storage systems. Data is ingested in its raw and unprocessed form, regardless of its structure or format. In a conventional data lake, data ingestion is relatively straightforward and flexible.
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