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A data analytics company uses BigQuery for its core data warehousing needs. They have several large tables, one of which stores historical clickstream data from their website. This table is petabytes in size and is queried frequently for aggregate analytics, but most queries only target data from the last 90 days. Older data is accessed infrequently, primarily for compliance and historical trend analysis. The company wants to optimize query performance and reduce storage costs for this table without losing access to historical data. Which BigQuery table optimization strategy should they implement?
- AImplement time-partitioning on the ingestion date column for the table.
- BMigrate the entire table to a different dataset with lower storage costs.
- CCreate a materialized view for the most frequently queried data.
- DConvert the table to a clustered table based on a frequently used dimension.
Show answer & explanationAnswer & explanation
Correct answer: A. Implement time-partitioning on the ingestion date column for the table.
Time-partitioning on the ingestion date allows BigQuery to prune partitions, scanning only the relevant data for queries (e.g., last 90 days), which significantly improves query performance and reduces the amount of data processed. It also leverages BigQuery's automatic long-term storage pricing for older partitions, reducing storage costs.
Why the other options are wrong
- B. Migrating a table to a different dataset does not inherently change its storage cost or improve query performance; BigQuery's storage costs are tiered based on data age, not dataset location.
- C. Materialized views can improve query performance for specific aggregate queries but do not directly address storage cost optimization for the entire petabyte-sized table or generalize to all queries on recent data.
- D. Clustering improves query performance by co-locating related data within partitions, but it doesn't reduce the overall storage cost or provide the same level of query pruning for time-based filters as partitioning.
BigQuery Table Optimization
BigQuery table optimization involves strategies like partitioning and clustering to improve query performance, reduce data scanned, and lower storage costs for large datasets.
- Partitioning divides a table into smaller segments by a column (e.g., date).
- Clustering organizes data within partitions by specified columns.
- Optimizations reduce query costs and execution time.
Memory trick: Partition, Cluster, Optimize: BigQuery's Best.