1
Single choice~70s
Which service is the serverless data warehouse for large-scale SQL analytics on Google Cloud?
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10 questions in Designing data processing systems
DESIGN_SYSTEMS
Designing data processing systems
10 questions in topicSelecting storage and processing services, and designing batch/streaming pipelines and schemas.
2
Single choice~90s
An IoT platform must store telemetry with millions of writes per second and low-latency operational reads. Which database fits BEST?
3
Single choice~90s
A system must ingest a continuous event stream, transform it, and make it queryable for near-real-time analytics. Which pipeline is the canonical design?
4
Single choice~90s
A team must migrate existing Apache Spark and Hive jobs to Google Cloud with minimal code changes. Which service fits BEST?
5
Single choice~90s
A new pipeline should be serverless, autoscaling, and handle batch and streaming with the same code. Which service should be chosen?
6
Single choice~110s
An application needs a relational database with strong consistency that scales horizontally across regions globally. Which service fits?
7
Single choice~90s
A team wants to build ETL pipelines visually (code-free) with prebuilt connectors, executing on Dataproc under the hood. Which service fits?
8
Single choice~90s
In a BigQuery-centric design, raw data is loaded first and transformed later with SQL in the warehouse. What is this approach called?
9
Single choice~100s
For analytical queries in BigQuery, which schema approach generally performs best?
10
Single choice~90s
Sales data is loaded once nightly for reporting and cost must be minimized. Which ingestion approach is most appropriate?