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Answer: Model the data in a series of append-only tables, partitioned by ingestion timestamp, and optimize queries using Z-ordering on relevant columns.
For high-frequency trading data ingestion into a lakehouse, modeling the data in append-only tables partitioned by ingestion timestamp and optimizing queries with Z-ordering on relevant columns is key. This approach minimizes ingestion latency by efficiently organizing data as it arrives, enabling immediate queryability for real-time analytics. Z-ordering further enhances performance by grouping related data together, reducing read latency during queries. This method stands out as the most efficient for ensuring both quick data ingestion and real-time analytics capabilities.
Author: LeetQuiz Editorial Team
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In the context of optimizing high-frequency trading data ingestion into a lakehouse, which data modeling technique ensures minimal ingestion latency while keeping the data immediately available for real-time analytics?
A
Ingest data directly into a traditional RDBMS, then periodically migrate to the lakehouse for long-term storage and analysis.
B
Use a single, monolithic table to store all incoming data, relying on the lakehouse‘s built-in optimization for real-time query performance.
C
Model the data in a series of append-only tables, partitioned by ingestion timestamp, and optimize queries using Z-ordering on relevant columns.
D
Implement a micro-batching approach with a complex ETL pipeline that transforms and loads data into partitioned Delta tables.
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