MapReduce VS Spark SQL
As of 30 October 2026, Spark SQL is named by 281 companies and MapReduce by 192; MapReduce skews to companies under 200 people (50% vs 36%), Spark SQL to companies over 1,000 (38% vs 29%).
As of 30 October 2026 · refreshed daily · counts move as websites, DNS and career sites are re-read.
The website-and-DNS footprint is not comparable between a marketing suite and a CRM: MapReduce's script and mail records show on hundreds of thousands of small sites, Spark SQL rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- MapReduce: 2–200 employees
- 50%
- Spark SQL: 2–200 employees
- 36%
- MapReduce: 1,000+ employees
- 29%
- Spark SQL: 1,000+ employees
- 38%
- Postings per company, 90 days
- 2.9 · 3.0
- Website or DNS footprint
- 0 · 0
Who chooses which
Share of each tool's companies in every segment. "Leans" marks a gap of more than two points.
| Segment | MapReduce | share | Spark SQL | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 5 | 3% | 3 | 1% | even |
| 2-10 employees | 5 | 3% | 18 | 6% | Spark SQL |
| 11-50 employees | 36 | 19% | 32 | 11% | MapReduce |
| 51-200 employees | 55 | 29% | 51 | 18% | MapReduce |
| 201-500 employees | 22 | 11% | 42 | 15% | Spark SQL |
| 501-1000 employees | 13 | 7% | 27 | 10% | Spark SQL |
| 1001-5000 employees | 26 | 14% | 53 | 19% | Spark SQL |
| 5001-10000 employees | 2 | 1% | 18 | 6% | Spark SQL |
| 10,001+ employees | 28 | 15% | 37 | 13% | even |
| Segment | MapReduce | share | Spark SQL | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 122 | 64% | 157 | 56% | MapReduce |
| Business & finance services | 34 | 18% | 58 | 21% | Spark SQL |
| Manufacturing & industry | 15 | 8% | 20 | 7% | even |
| Public, community & associations | 5 | 3% | 4 | 1% | even |
| Consumer services & leisure | 4 | 2% | – | – | outside top 12 |
| Automotive | 3 | 2% | 2 | 1% | even |
| Health care | 3 | 2% | 4 | 1% | even |
| Media & content | 2 | 1% | 7 | 2% | even |
| Utilities & infrastructure | 2 | 1% | 10 | 4% | Spark SQL |
| Commerce & trade | 1 | 1% | 11 | 4% | Spark SQL |
| Hospitality & travel | 1 | 1% | 1 | 0% | even |
| Agriculture & forestry | – | – | 3 | 1% | outside top 12 |
| Transport, logistics & rental | – | – | 3 | 1% | outside top 12 |
| Segment | MapReduce | share | Spark SQL | share | Leans |
|---|---|---|---|---|---|
| United States | 156 | 81% | 183 | 65% | MapReduce |
| India | 6 | 3% | 10 | 4% | even |
| Germany | 4 | 2% | 9 | 3% | even |
| United Kingdom | 4 | 2% | 20 | 7% | Spark SQL |
| Australia | 3 | 2% | 3 | 1% | even |
| Italy | 3 | 2% | 4 | 1% | even |
| Singapore | 3 | 2% | – | – | outside top 12 |
| Austria | 2 | 1% | – | – | outside top 12 |
| United Arab Emirates | 1 | 1% | – | – | outside top 12 |
| Switzerland | 1 | 1% | – | – | outside top 12 |
| Colombia | 1 | 1% | – | – | outside top 12 |
| Cyprus | 1 | 1% | – | – | outside top 12 |
| Canada | – | – | 9 | 3% | outside top 12 |
| Belgium | – | – | 5 | 2% | outside top 12 |
| Netherlands | – | – | 5 | 2% | outside top 12 |
| Brazil | – | – | 4 | 1% | outside top 12 |
| France | – | – | 4 | 1% | outside top 12 |
| Poland | – | – | 4 | 1% | outside top 12 |
| Segment | MapReduce | share | Spark SQL | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 839 | 66% | 910 | 81% | Spark SQL |
| Software engineering | 244 | 19% | 103 | 9% | MapReduce |
| IT operations & business systems | 29 | 2% | 17 | 2% | even |
| Product, program & project management | 11 | 1% | 7 | 1% | even |
| Education & training | 5 | 0% | – | – | outside top 12 |
| Engineering (non-software) | 5 | 0% | 5 | 0% | even |
| Finance & accounting | 4 | 0% | 4 | 0% | even |
| General management & consulting | 3 | 0% | 4 | 0% | even |
| Logistics warehouse and transport | 2 | 0% | – | – | outside top 12 |
| Human resources | 1 | 0% | – | – | outside top 12 |
| Sales | 1 | 0% | 1 | 0% | even |
| Unclassified | 1 | 0% | – | – | outside top 12 |
| Quality, safety & environment | – | – | 2 | 0% | outside top 12 |
| Construction and civil | – | – | 1 | 0% | outside top 12 |
| Hospitality and food service | – | – | 1 | 0% | outside top 12 |
| Legal, compliance & risk | – | – | 1 | 0% | outside top 12 |
The companies behind each column
Companies using MapReduce →
Companies using Spark SQL →
distributed processing ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
Spark SQL is named by 281 companies in job postings and MapReduce by 192; 21 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/mapreduce-vs-spark-sql)
Read from the source, not self-reported
Every number on this page can be traced to a page we crawled or a posting we read. Full methodology →
- Job postingsRead hourly from career sites. A tool counts when the employer's own text names it as the team's system; both tools can count for one company.
- Website and DNSScripts and embeds on the company's pages, plus SPF, DKIM, MX and CNAME records of its domain that point at the vendor.
- SegmentsCountry, size and industry from the company profile built from its website.
- Not a reviewNo ratings, prices or feature claims. For those, read the vendors.
MapReduce vs Spark SQL: questions
Which is more used, MapReduce or Spark SQL?
By companies naming it in their own job postings, Spark SQL (281) over MapReduce (192). By website-and-DNS footprint, Spark SQL (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses MapReduce?
50% of MapReduce companies have 2 to 200 employees, against 36% for Spark SQL. 29% of MapReduce companies have over 1,000 employees, against 38% for Spark SQL.
Do companies run both?
21 companies name both in their postings, usually MapReduce in marketing and Spark SQL in sales, or during a migration.
Where do these numbers come from?
From the employer's own text in job postings read hourly from career sites, and from scripts found on company websites in our crawl. No vendor data, no surveys.
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