How to Choose SQL Server over Redshift in PostgreSQL

Galaxy Glossary

Why choose SQL Server over Amazon Redshift?

Explains when and why teams should prefer Microsoft SQL Server instead of Amazon Redshift for their workloads.

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Description

Why pick SQL Server over Amazon Redshift?

SQL Server shines on workloads that need strict ACID guarantees, frequent single-row updates, and mixed OLTP + analytics. Redshift is columnar and optimized for large scans. If your ecommerce stack inserts and updates orders all day, SQL Server will be faster and cheaper than Redshift’s massive-parallel clusters.

Does SQL Server handle mixed workloads better?

Yes. SQL Server’s row-store indexes and lock manager perform well for point lookups while still supporting reporting. Redshift requires Spectrum or ETL into another store for true OLTP, adding cost and latency.

How does SQL Server pricing compare for small datasets?

SQL Server’s per-core or Azure SQL’s serverless model scales down nicely. Redshift charges per node—even a single ra3.xlplus idles at >$3K/year. Teams under 2 TB often overpay on Redshift.

What SQL features exist in SQL Server but not Redshift?

Key features include MERGE with OUTPUT, temporal tables, CLR functions, and full-text search. Redshift lacks these, forcing workarounds or extra services.

When should you still choose Redshift?

Pick Redshift for multi-terabyte star schemas, low concurrency reporting, and separation from transactional systems. Use Spectrum to query S3 data lakes at petabyte scale.

Which SQL syntax difference matters most?

The MERGE statement. SQL Server supports a native upsert with conditional logic; Redshift does not.

Best practice: size for workload, not hype

Benchmark your real query mix. Many teams discover a single SQL Server node meets both transactional and analytic needs, avoiding Redshift’s operational overhead.

Code example: upsert orders efficiently

The following MERGE runs in SQL Server but fails in Redshift without rewrite.

Why How to Choose SQL Server over Redshift in PostgreSQL is important

How to Choose SQL Server over Redshift in PostgreSQL Example Usage


-- Upsert two orders; runs in SQL Server, not in Redshift
MERGE Orders AS tgt
USING (VALUES
   (1, 1001, '2024-06-01', 250.00),
   (2, 1002, '2024-06-01', 125.00)
) AS src(id, customer_id, order_date, total_amount)
ON tgt.id = src.id
WHEN MATCHED THEN UPDATE SET
   total_amount = src.total_amount
WHEN NOT MATCHED THEN INSERT (id, customer_id, order_date, total_amount)
   VALUES (src.id, src.customer_id, src.order_date, src.total_amount);

How to Choose SQL Server over Redshift in PostgreSQL Syntax


MERGE Orders AS tgt
USING (VALUES
   (1, 1001, '2024-06-01', 250.00),
   (2, 1002, '2024-06-01', 125.00)
) AS src(id, customer_id, order_date, total_amount)
ON tgt.id = src.id
WHEN MATCHED THEN
   UPDATE SET
       customer_id  = src.customer_id,
       order_date   = src.order_date,
       total_amount = src.total_amount
WHEN NOT MATCHED THEN
   INSERT (id, customer_id, order_date, total_amount)
   VALUES (src.id, src.customer_id, src.order_date, src.total_amount)
OUTPUT $action, INSERTED.*;

Common Mistakes

Assuming Redshift can replace OLTP databases. Redshift’s columnar storage makes single-row inserts slow. Keep transactional workloads on SQL Server or another row-store engine.
Ignoring total cost of ownership. Redshift clusters incur fixed node costs and require ETL and monitoring. SQL Server’s managed offerings often reduce both expenses and operational effort.

Frequently Asked Questions (FAQs)

Is SQL Server still relevant for analytics?

Yes. Columnstore indexes and PolyBase let SQL Server handle large analytical queries while preserving OLTP speed.

Can I migrate later from SQL Server to Redshift?

Yes, but avoid features Redshift lacks, such as CLR functions or full MERGE, to simplify future migration.

Does SQL Server support columnar storage like Redshift?

SQL Server’s clustered columnstore index offers compressed, memory-optimized analytics inside the same engine.

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