Data Management Flashcards

What is the role of data versioning in lakehouse systems?

Answer: To track and revert changes

Data versioning in lakehouse systems provides a historical record of all changes made to the data over time. This capability is essential for auditing, compliance, and debugging, as it allows users to track data modifications, understand data lineage, and revert to previous data states if errors or inconsistencies occur.

Which lakehouse feature enables schema enforcement?

Answer: Schema enforcement

Schema enforcement is a crucial lakehouse feature, often provided by formats like Delta Lake, that ensures all new data written to a table conforms to its predefined schema. This mechanism prevents the ingestion of malformed or inconsistent data, thereby maintaining high data quality and reliability for downstream analytics and applications.

What is the purpose of a data catalog in a lakehouse?

Answer: Index metadata and support data discovery

A data catalog in a lakehouse acts as a centralized inventory of all data assets, their metadata, and lineage. Its purpose is to index this metadata and support data discovery, helping users find, understand, and trust data by providing descriptions, usage information, and quality metrics, thereby facilitating efficient data utilization and governance.

Which component manages changes in data structure over time?

Answer: Schema evolution

Schema evolution is a key component that manages changes in data structure over time without disrupting existing queries or applications. This flexibility allows organizations to adapt to evolving data requirements, such as adding new columns or changing data types, while maintaining data integrity and historical access within the lakehouse.

Why is ACID compliance important in lakehouse data management?

Answer: To support reliable transactions

ACID (Atomicity, Consistency, Isolation, Durability) compliance is vital in lakehouse data management because it guarantees that data transactions are processed reliably and consistently, even during concurrent operations or system failures. This ensures data integrity and trustworthiness, which is critical for accurate analytics and machine learning workloads.

What benefit does data compaction provide in a lakehouse?

Answer: Reduces latency and optimizes storage

Data compaction in a lakehouse involves merging numerous small files into fewer, larger files. This process significantly reduces the overhead associated with managing many small files, leading to faster query execution times (reduced latency) and more efficient storage utilization, ultimately optimizing overall system performance.