How to BigQuery use cases in PostgreSQL

Galaxy Glossary

What are the most valuable BigQuery use cases for e-commerce analytics?

BigQuery excels at ad-hoc analytics, real-time reporting, and ML on large datasets without managing infrastructure.

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Description

Example H2

Example H3

What problems does BigQuery solve for e-commerce?

BigQuery handles terabytes of clickstream, order, and customer data while auto-scaling storage and compute. It eliminates manual sharding, vacuuming, and index tuning.

When should I choose BigQuery over PostgreSQL?

Use BigQuery for analytical workloads that scan large, append-only tables, power dashboards, or train ML models.Keep PostgreSQL for OLTP tasks like order writes and transactions.

How do I query customer revenue across orders?

SELECT c.id, c.name, SUM(o.total_amount) AS lifetime_value FROM `shop.Customers` c JOIN `shop.Orders` o USING (id) GROUP BY c.id, c.name ORDER BY lifetime_value DESC;

How do partitions and clusters cut costs?

Partition by DATE(order_date) to restrict scans to recent data; cluster by customer_id to keep related rows close, speeding equality filters.

How can I join millions of rows efficiently?

Always pre-filter each table with partition predicates, then JOIN.If data is still skewed, use HASH_JOIN hints or break the job into smaller queries.

How do I build a real-time sales dashboard?

Ingest events via BigQuery Streaming API into a partitioned shop.Orders table, then schedule a materialized view that aggregates totals each minute.

Can BigQuery run machine learning on my orders?

Yes. CREATE MODEL lets you predict churn, recommend products, or forecast sales directly in SQL using BigQuery ML.

Best practices to keep queries fast

1) Select columns explicitly. 2) Filter by partition columns first.3) Store numeric IDs not strings. 4) Use materialized views for repeated aggregates.

What are common mistakes?

Scanning unpartitioned tables, SELECT *, and forgetting to set location for temp functions inflate cost and latency.

Why How to BigQuery use cases in PostgreSQL is important

How to BigQuery use cases in PostgreSQL Example Usage


-- Customer lifetime value segmented by acquisition month
WITH first_purchase AS (
  SELECT customer_id,
         MIN(order_date) AS first_order
  FROM   `shop.Orders`
  GROUP  BY customer_id
)
SELECT FORMAT_DATE('%Y-%m', first_order) AS cohort,
       COUNT(DISTINCT c.id)              AS customers,
       SUM(o.total_amount)               AS revenue
FROM   first_purchase fp
JOIN   `shop.Customers` c ON c.id = fp.customer_id
JOIN   `shop.Orders`   o ON o.customer_id = c.id
GROUP  BY cohort
ORDER  BY cohort;

How to BigQuery use cases in PostgreSQL Syntax


-- SELECT with JOINs
SELECT c.id, c.name, o.id AS order_id, o.total_amount
FROM `shop.Customers`  AS c
JOIN `shop.Orders`     AS o ON c.id = o.customer_id
WHERE o.order_date BETWEEN '2024-06-01' AND '2024-06-30';

-- Create a partitioned & clustered table
CREATE TABLE `shop.Orders` (
  id            INT64,
  customer_id   INT64,
  order_date    DATE,
  total_amount  NUMERIC
)
PARTITION BY DATE(order_date)
CLUSTER BY customer_id;

-- External table for product catalog in GCS
CREATE EXTERNAL TABLE `shop.Products`
WITH PARTITION COLUMNS
OPTIONS (
  format = 'PARQUET',
  uris   = ['gs://data/products/*.parquet']
);

-- Train a churn-prediction model
CREATE OR REPLACE MODEL `shop.churn_model`
OPTIONS (model_type='logistic_reg') AS
SELECT customer_id,
       SUM(total_amount)    AS spend,
       COUNT(order_id)      AS orders,
       MAX(order_date)      AS last_order,
       churned              AS label
FROM   `shop.OrderFeatures`;

Common Mistakes

Running SELECT * on large, unpartitioned tables. This forces BigQuery to scan every column and row, inflating costs. Fix by listing only required columns and partition filters.
Ignoring partition and cluster design. Dumping data into a single table without DATE partitions prevents pruning and slows queries. Re-create tables with PARTITION BY and CLUSTER BY clauses.

Frequently Asked Questions (FAQs)

Is BigQuery a replacement for my transactional database?

No. BigQuery is optimized for analytics, not row-level updates or high-concurrency transactions. Keep PostgreSQL or another OLTP store for day-to-day writes.