How to Decide Between ParadeDB and BigQuery in PostgreSQL

Galaxy Glossary

Why use ParadeDB over BigQuery for analytics?

ParadeDB offers PostgreSQL-native, low-latency analytics and vector search at a fraction of BigQuery’s cost.

Welcome to the Galaxy, Guardian!
You'll be receiving a confirmation email

Follow us on twitter :)

Oops! Something went wrong while submitting the form.

Description

Example H2

Example H3

Why pick ParadeDB instead of BigQuery?

ParadeDB runs on PostgreSQL, so you keep ACID guarantees, extensions like pgvector, and familiar tooling. BigQuery is serverless but charges per scanned byte, making exploratory work expensive. ParadeDB sits in your cloud VPC, giving predictable infrastructure costs and easier governance.

What performance gains can I expect?

ParadeDB stores data locally and supports columnar storage plus hybrid row/column execution. Latency drops from seconds to milliseconds for filtered joins on ecommerce tables. Vector indexes enable semantic product search without external services.

Does ParadeDB cut query latency?

Yes. Benchmarks on 100M-row Orders show 50–80 ms response versus BigQuery’s 1–3 s cold starts, because ParadeDB avoids remote shuffling and uses CPU-cache-friendly formats.

How do I migrate my ecommerce data?

1. Export BigQuery tables to GCS as Avro or Parquet. 2. Use paradedb import or COPY to load into ParadeDB.
3. Re-create indexes and constraints. 4. Validate row counts and sample queries.

Best practices when moving from BigQuery to ParadeDB

Create partitioned tables for event streams, use CLUSTER on order_date, and add pgvector indexes for embeddings. Monitor with pg_stat_statements and tune work_mem for joins.

Cost comparison FAQs

ParadeDB bills for compute and storage you provision. BigQuery charges per TB scanned plus storage. For iterative dev, ParadeDB typically cuts cost 60–80% by eliminating pay-per-query pricing.

Why How to Decide Between ParadeDB and BigQuery in PostgreSQL is important

How to Decide Between ParadeDB and BigQuery in PostgreSQL Example Usage


-- Same-day revenue snapshot in ParadeDB
SELECT SUM(total_amount) AS today_revenue
FROM orders
WHERE order_date::date = CURRENT_DATE;

How to Decide Between ParadeDB and BigQuery in PostgreSQL Syntax


-- ParadeDB: vector similarity search on Products
CREATE EXTENSION IF NOT EXISTS pgvector;

CREATE TABLE products (
  id           SERIAL PRIMARY KEY,
  name         TEXT,
  price        NUMERIC,
  stock        INT,
  embedding    VECTOR(768)
);

-- Vector index for fast similarity
CREATE INDEX idx_products_embedding ON products USING hnsw (embedding vector_l2_ops);

-- Query for top 5 similar products to an embedding
SELECT id, name, price
FROM products
ORDER BY embedding <-> '[0.12,0.42, ...]'::vector
LIMIT 5;

-- BigQuery equivalent (approximate, no native vector)
CREATE MODEL `demo.product_knn` OPTIONS(model_type='KNN', input_label_cols=['id']) AS
SELECT * FROM `project.dataset.products`;

SELECT * FROM ML.PREDICT(MODEL `demo.product_knn`,
  (SELECT ARRAY<[0.12,0.42,...]> AS embedding))
LIMIT 5;

Common Mistakes

Ignoring extensions: Users forget to install pgvector or timescaledb, then fail to see performance gains. Fix by running CREATE EXTENSION before loading data.
Copy-pasting BigQuery SQL without type tweaks: ParadeDB needs SERIAL/BIGINT for auto IDs and TIMESTAMP WITH TIME ZONE. Adjust schemas during migration.