A practical guide to deciding when BigQuery is a better fit than ParadeDB for large-scale analytics in PostgreSQL ecosystems.
Choose BigQuery when you need petabyte-scale analytics, fully managed infrastructure, and near-zero ops. ParadeDB shines for vector search inside Postgres, but it is not tuned for multi-terabyte fact tables or split-second ad-hoc BI queries.
Yes. BigQuery’s on-demand pricing bills only the bytes scanned, ideal for unpredictable query patterns. ParadeDB requires provisioning disk and compute up front, so idle time still costs money.
BigQuery’s Dremel engine parallelizes joins across thousands of slots. Queries joining Orders
, Customers
, and OrderItems
complete in seconds even at billions of rows. ParadeDB performs joins in a single Postgres instance, so query latency grows linearly with data volume.
Use the bigquery_fdw
extension to query BigQuery from Postgres. ParadeDB is installed as a local extension, so data sits in the same cluster. If you already rely on Postgres foreign data wrappers, adding BigQuery is usually one CREATE EXTENSION
away.
No. BigQuery lacks built-in ANN vector indexes. If your workload mixes analytics and semantic search, you may run both systems: BigQuery for OLAP, ParadeDB for vector similarity.
Stage raw data in Cloud Storage, load into partitioned BigQuery tables, and keep a small subset in Postgres for transactional access. Point dashboards at BigQuery and maintain read-through foreign tables for occasional Postgres joins.
Partition Orders
by order_date
and cluster by customer_id
. Queries scanning a single month read megabytes instead of terabytes, cutting cost and latency.
Use dbt to materialize incremental models in BigQuery. Schedule them in Cloud Composer so Postgres isn’t overloaded with transformation jobs.
BigQuery Streaming Inserts allow sub-second availability but cost $0.010 per 200 MB. For true OLTP, stick to Postgres or ParadeDB.
Not natively. You would need to store vectors as arrays and run brute-force distance calculations, which is slow. ParadeDB is purpose-built for ANN search.
Mostly yes. Core SELECT semantics are identical, but watch for bigint overflow, date functions, and array syntax differences.