My company is planning a major migration from an on-premise Hadoop cluster to a managed data warehouse. We are debating between Google Cloud BigQuery and AWS Redshift. Based on current <Cloud (AWS/Azure/GCP) Computing> benchmarks, which one performs better for ad-hoc queries on petabyte-scale datasets? We are particularly interested in the serverless nature of BigQuery vs the cluster-based management of Redshift.
3 answers
The performance difference between BigQuery and Redshift often comes down to the nature of your queries. BigQuery is truly serverless and separates compute from storage, allowing it to scale nearly infinitely for massive ad-hoc queries without any manual configuration. Redshift, even with its RA3 nodes, still requires some level of cluster management and performance tuning (like choosing distribution keys). If your workload is predictable and constant, Redshift can be more cost-effective. However, for massive, unpredictable datasets, BigQuery’s ability to scan terabytes in seconds is hard to beat.
Deborah, that's interesting. But doesn't Redshift Spectrum offer a similar 'serverless' query capability for data sitting in S3?
Consider the pricing model too; BigQuery's on-demand pricing can get expensive if you have poorly optimized queries scanning full tables.
Spot on, Ashley. Partitioning and clustering your data in BigQuery is absolutely essential to keep those 'cost per query' numbers from skyrocketing unexpectedly.
It does, Ryan, but Spectrum still relies on an underlying cluster to manage the execution. BigQuery is more of a 'plug and play' experience. If you don't want to spend time managing concurrency scaling or vacuuming tables to keep performance high, BigQuery’s automated backend is a major relief for data engineering teams.