Google Cloud Dataflow VS MapReduce
As of 30 October 2026, MapReduce is named by 192 companies and Google Cloud Dataflow by 362; Google Cloud Dataflow skews to companies under 200 people (38% vs 50%), MapReduce to companies over 1,000 (29% vs 38%).
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: Google Cloud Dataflow's script and mail records show on hundreds of thousands of small sites, MapReduce rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Google Cloud Dataflow: 2–200 employees
- 38%
- MapReduce: 2–200 employees
- 50%
- Google Cloud Dataflow: 1,000+ employees
- 38%
- MapReduce: 1,000+ employees
- 29%
- Postings per company, 90 days
- 3.6 · 2.9
- 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 | Google Cloud Dataflow | share | MapReduce | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 3 | 1% | 5 | 3% | even |
| 2-10 employees | 20 | 6% | 5 | 3% | Google Cloud Dataflow |
| 11-50 employees | 44 | 12% | 36 | 19% | MapReduce |
| 51-200 employees | 72 | 20% | 55 | 29% | MapReduce |
| 201-500 employees | 55 | 15% | 22 | 11% | Google Cloud Dataflow |
| 501-1000 employees | 30 | 8% | 13 | 7% | even |
| 1001-5000 employees | 57 | 16% | 26 | 14% | Google Cloud Dataflow |
| 5001-10000 employees | 19 | 5% | 2 | 1% | Google Cloud Dataflow |
| 10,001+ employees | 62 | 17% | 28 | 15% | Google Cloud Dataflow |
| Segment | Google Cloud Dataflow | share | MapReduce | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 186 | 51% | 122 | 64% | MapReduce |
| Business & finance services | 76 | 21% | 34 | 18% | Google Cloud Dataflow |
| Commerce & trade | 21 | 6% | 1 | 1% | Google Cloud Dataflow |
| Manufacturing & industry | 21 | 6% | 15 | 8% | MapReduce |
| Utilities & infrastructure | 15 | 4% | 2 | 1% | Google Cloud Dataflow |
| Consumer services & leisure | 11 | 3% | 4 | 2% | even |
| Media & content | 11 | 3% | 2 | 1% | even |
| Public, community & associations | 6 | 2% | 5 | 3% | even |
| Health care | 5 | 1% | 3 | 2% | even |
| Property & construction | 4 | 1% | – | – | outside top 12 |
| Transport, logistics & rental | 3 | 1% | – | – | outside top 12 |
| Automotive | 2 | 1% | 3 | 2% | even |
| Hospitality & travel | – | – | 1 | 1% | outside top 12 |
| Segment | Google Cloud Dataflow | share | MapReduce | share | Leans |
|---|---|---|---|---|---|
| United States | 209 | 58% | 156 | 81% | MapReduce |
| United Kingdom | 30 | 8% | 4 | 2% | Google Cloud Dataflow |
| France | 16 | 4% | – | – | outside top 12 |
| India | 15 | 4% | 6 | 3% | even |
| Germany | 13 | 4% | 4 | 2% | even |
| Canada | 10 | 3% | – | – | outside top 12 |
| Sweden | 10 | 3% | – | – | outside top 12 |
| Spain | 6 | 2% | – | – | outside top 12 |
| Poland | 6 | 2% | – | – | outside top 12 |
| Australia | 5 | 1% | 3 | 2% | even |
| Belgium | 4 | 1% | – | – | outside top 12 |
| Brazil | 4 | 1% | – | – | outside top 12 |
| Italy | – | – | 3 | 2% | outside top 12 |
| 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 |
| Segment | Google Cloud Dataflow | share | MapReduce | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 1,141 | 64% | 839 | 66% | even |
| Software engineering | 266 | 15% | 244 | 19% | MapReduce |
| IT operations & business systems | 87 | 5% | 29 | 2% | Google Cloud Dataflow |
| Healthcare clinical | 29 | 2% | – | – | outside top 12 |
| Product, program & project management | 14 | 1% | 11 | 1% | even |
| Sales | 13 | 1% | 1 | 0% | even |
| Finance & accounting | 6 | 0% | 4 | 0% | even |
| Customer service & success | 4 | 0% | – | – | outside top 12 |
| Engineering (non-software) | 4 | 0% | 5 | 0% | even |
| Human resources | 3 | 0% | 1 | 0% | even |
| Education & training | 2 | 0% | 5 | 0% | even |
| Unclassified | 2 | 0% | 1 | 0% | even |
| General management & consulting | – | – | 3 | 0% | outside top 12 |
| Logistics warehouse and transport | – | – | 2 | 0% | outside top 12 |
The companies behind each column
Companies using Google Cloud Dataflow →
Companies using MapReduce →
distributed processing ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
MapReduce is named by 192 companies in job postings and Google Cloud Dataflow by 362; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/google-cloud-dataflow-vs-mapreduce)
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.
Google Cloud Dataflow vs MapReduce: questions
Which is more used, Google Cloud Dataflow or MapReduce?
By companies naming it in their own job postings, Google Cloud Dataflow (362) over MapReduce (192). By website-and-DNS footprint, MapReduce (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Google Cloud Dataflow?
38% of Google Cloud Dataflow companies have 2 to 200 employees, against 50% for MapReduce. 38% of Google Cloud Dataflow companies have over 1,000 employees, against 29% for MapReduce.
Do companies run both?
0 companies name both in their postings, usually Google Cloud Dataflow in marketing and MapReduce 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.
Get either list your way
The same data as a search, over MCP and the API, as bulk files, or pushed to you as it changes.
Every company on either tool,
with the person who owns it
Filter on Google Cloud Dataflow or MapReduce. Start free, no card.