Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You use BigQuery as your main data warehouse. By time, your tables start to get bigger and selecting from these tables result in scanning many rows which increases the cost of queries running on them. You want to find a way to reduce the costs of queries scanning through your big tables. What should you do? (Choose 2)
A. Use LIMIT when running SELECT statements on the tables.
B. Use partitioning to split data into partitions by columns most used for filtering data.
C. Set BigQuery to limit scanning data to certain size.
D. Use sharding to split data into several tables.
Answers: B & D.
A partitioned table is a special table that is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can improve query performance, and you can control costs by reducing the number of bytes read by a query.
There are two types of table partitioning in BigQuery:
- Tables partitioned by ingestion time: Tables partitioned based on the data’s ingestion (load) date or arrival date.
- Partitioned tables: Tables that are partitioned based on a TIMESTAMP or DATE column.
As an alternative to partitioned tables, you can shard tables using a time-based naming approach such as [PREFIX]_YYYYMMDD. This is referred to as creating date-sharded tables.
Source(s):
BigQuery – Introduction to Partitioned Tables: https://cloud.google.com/bigquery/docs/partitioned- tables#partitioning_versus_sharding
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