PG vs MS — Evidence-Based Comparisons of PostgreSQL, SQL Server, and the Database Decisions That Shape Production Systems

Working paper Cited in 5 sources

Data Warehousing: Complete Overview

Research area Database / SQL

Abstract

Explore data warehousing essentials: its components, architectures, and strategic implementation for robust business intelligence and data-driven decisions.

Author
Hannah Kowalski
Published
June 2026
Reading time
6 minutes
Read the full paper →
Library

Recent publications

14 papers, ordered chronologically.

  1. 01 Data Warehousing Comparison Guide Choosing a data warehouse requires evaluating scalability, cost, integration, and management. Database / SQL Hannah Kowalski Jun 2026
  2. 02 Data Warehousing Trends to Watch The data warehousing landscape evolves with cloud adoption, AI, and new architectures. Grasping these trends is crucial for strategic data infrastructure and. Database / SQL Hannah Kowalski Jun 2026
  3. 03 Data Warehousing Best Practices Implementing effective data warehousing best practices ensures data integrity, optimizes query performance, and supports strategic business intelligence. Database / SQL Hannah Kowalski Jun 2026
  4. 04 How Data Warehousing Works Understand how data warehousing centralizes disparate data for analytics, supports business intelligence, and drives informed strategic decisions. Database / SQL Hannah Kowalski Jun 2026
  5. 05 Data Warehousing Mistakes to Avoid Avoid critical data warehousing pitfalls that lead to costly inefficiencies, poor data quality, and compliance risks. Database / SQL Hannah Kowalski Jun 2026
  6. 06 Data Warehousing Tips Unlock competitive advantage with these essential data warehousing tips for strategic planning, data integration, performance, security, and maintenance. Database / SQL Hannah Kowalski Jun 2026
  7. 07 Data Warehousing Examples Explore real-world data warehousing examples, from traditional on-premises setups to modern cloud-native and data lakehouse architectures, to inform your. Database / SQL Hannah Kowalski May 2026
  8. 08 Data Warehousing Checklist This data warehousing checklist guides businesses through essential considerations for planning, implementing, and optimizing a robust data infrastructure. Database / SQL Hannah Kowalski May 2026
  9. 09 Data Warehousing: Beginner Guide Understand the fundamentals of data warehousing, its core components, and how it drives strategic business decisions. Database / SQL Hannah Kowalski May 2026
  10. 10 Database Backups: Complete Overview Protect critical business data and ensure continuity with a robust database backup strategy. Database / SQL Hannah Kowalski May 2026
  11. 11 Database Backups Comparison Guide Navigate database backup methodologies like full, differential, and incremental, understanding their trade-offs for storage, speed, and recovery. Database / SQL Hannah Kowalski May 2026
  12. 12 Database Backups Trends to Watch Explore critical database backup trends, from immutable storage and AI-driven automation to cyber resilience and hybrid cloud strategies, for robust data. Database / SQL Hannah Kowalski May 2026
  13. 13 Database Backups Best Practices Implement a robust database backup strategy by understanding RPO/RTO, applying the 3-2-1 rule, and regularly testing recovery procedures for business continuity. Database / SQL Hannah Kowalski May 2026
  14. 14 How Database Backups Works Understanding how database backups work is critical for data integrity and business continuity, ensuring rapid recovery from unforeseen data loss events. Database / SQL Hannah Kowalski May 2026
For the record

How this site works

Methodology

Each piece is built from primary sources, published research, and direct interviews. We document every step from question to citation so readers can follow the chain themselves.

Sources

We rely on regulatory filings, peer-reviewed journals, official statistics, and on-the-record commentary. Anonymous sourcing requires a second confirmation.

Disclosures

No external funding underwrites this site. The author has no positions in any companies discussed unless explicitly stated. All errors are corrected and dated in-place.

From the archive
Vendor comparisons of database systems are dominated by partial methodologies.¹

¹ Hannah Kowalski, PostgreSQL vs SQL Server: Documented Benchmark Across Six Workload Types (2026).

PG vs MS
About the author

PG vs MS

PG vs MS — *evidence-based comparisons of PostgreSQL and SQL Server*.

Affiliation Warsaw, Poland

Database vendor comparisons online are dominated by content with conflicting incentives — vendor blogs, sponsored content, affiliate-influenced reviews. The result is that engineers seeking honest analysis face an information environment that systematically favors specific products rather than presenting evidence-based assessment.

This publication produces evidence-based comparisons of PostgreSQL, SQL Server, and the database decisions that shape production systems. Each piece documents specific benchmarks, methodology, and trade-offs. Vendor relationships are disclosed when present and never determine recommendations. The objective is to give engineering teams the honest information they need to make defensible database decisions.

Selected recent papers

  1. Data Warehousing: Complete Overview (Jun 2026)
  2. Data Warehousing Comparison Guide (Jun 2026)
  3. Data Warehousing Trends to Watch (Jun 2026)
  4. Data Warehousing Best Practices (Jun 2026)
  5. How Data Warehousing Works (Jun 2026)
Subscribe

Get new working papers

A short note when a new paper is published. References, abstracts, and methodology summaries.