How to choose BigQuery over Oracle

Galaxy Glossary

Why should my team pick BigQuery over Oracle for analytics workloads?

Explains practical reasons, syntax differences, and migration steps when selecting Google BigQuery instead of Oracle for analytics workloads.

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Description

Why choose BigQuery instead of Oracle?

BigQuery eliminates hardware management, scales queries to petabytes automatically, and uses on-demand pricing, so you pay only for bytes processed. Oracle demands up-front licensing, ongoing tuning, and capacity planning.

How does BigQuery pricing compare?

BigQuery storage is $0.02/GB/month; query cost is $5 per scanned TB. Oracle licensing is CPU-based with annual support. Variable costs make BigQuery cheaper for spiky analytics.

What SQL changes are needed?

BigQuery follows ANSI 2011, supports ARRAY and STRUCT types, and requires back-tick identifiers. Oracle-specific PL/SQL, CONNECT BY, or ROWNUM need refactoring to BigQuery functions like GENERATE_ARRAY and LIMIT.

How do I migrate data?

Export Oracle tables to Cloud Storage as CSV/AVRO, then use bq load. Partition and cluster destination tables during load to keep future queries cheap.

Example migration workflow

# Oracle -> CSV oexpdp userid=system schemas=SHOP tables=ORDERS dumpfile=orders.dmp # Upload to GCS gsutil cp orders.csv gs://shop_stage/ # Load to BigQuery bq load --source_format=CSV --autodetect shop.Orders gs://shop_stage/orders.csv

Performance tuning differences?

BigQuery uses columnar storage and automatic parallelism; no indexes needed. Oracle requires indexes and partitioning strategies. Focus on partitioned, clustered BigQuery tables and avoid SELECT *.

Security & governance?

BigQuery integrates with IAM, VPC-SC, and CMEK. Row-level security is declarative. Oracle relies on DB roles and VPD policies, adding administrative overhead.

Why How to choose BigQuery over Oracle is important

How to choose BigQuery over Oracle Example Usage


-- Identify out-of-stock products and their last order date in BigQuery
SELECT p.id, p.name, MAX(o.order_date) AS last_order
FROM `shop.Products`   AS p
JOIN `shop.OrderItems` AS oi ON oi.product_id = p.id
JOIN `shop.Orders`     AS o  ON o.id = oi.order_id
WHERE p.stock = 0
GROUP BY p.id, p.name
ORDER BY last_order DESC;

How to choose BigQuery over Oracle Syntax


-- BigQuery Standard SQL vs Oracle example
-- Fetch top spending customers in 2023
-- BigQuery
SELECT c.id, c.name, SUM(o.total_amount) AS revenue
FROM `shop.Customers` AS c
JOIN `shop.Orders`   AS o ON o.customer_id = c.id
WHERE EXTRACT(YEAR FROM o.order_date) = 2023
GROUP BY c.id, c.name
ORDER BY revenue DESC
LIMIT 10;

-- Oracle
SELECT c.id, c.name, SUM(o.total_amount) AS revenue
FROM Customers c
JOIN Orders   o ON o.customer_id = c.id
WHERE EXTRACT(YEAR FROM o.order_date) = 2023
GROUP BY c.id, c.name
ORDER BY revenue DESC
FETCH FIRST 10 ROWS ONLY;

Common Mistakes

Assuming Oracle SQL runs unchanged in BigQuery. BigQuery lacks PL/SQL and some analytic functions. Refactor with Standard SQL equivalents and UDFs.
Ignoring cost controls. Running SELECT * on terabytes spikes bills. Always project needed columns, use partitions, and set cost-aware quotas.

Frequently Asked Questions (FAQs)

Is BigQuery always cheaper than Oracle?

Not always. Constant, predictable workloads on existing Oracle hardware may be cheaper on-prem. BigQuery wins when usage is bursty or scaling rapidly.

Can I keep transactional workloads on Oracle?

Yes. Many teams run OLTP on Oracle and stream changes to BigQuery for analytics using Dataflow, Fivetran, or GoldenGate.

How long does migration take?

Small schemas migrate in days. Terabyte-scale systems require phased loads, validation, and SQL conversion; plan for weeks or months.

Want to learn about other SQL terms?

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