Azure Data Factory VS IBM DataStage
As of 30 October 2026, IBM DataStage is named by 168 companies and Azure Data Factory by 1,655; Azure Data Factory skews to companies under 200 people (33% vs 41%), IBM DataStage to companies over 1,000 (41% vs 40%).
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: Azure Data Factory's script and mail records show on hundreds of thousands of small sites, IBM DataStage rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Azure Data Factory: 2–200 employees
- 33%
- IBM DataStage: 2–200 employees
- 41%
- Azure Data Factory: 1,000+ employees
- 40%
- IBM DataStage: 1,000+ employees
- 41%
- Postings per company, 90 days
- 3.2 · 5.3
- 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 | Azure Data Factory | share | IBM DataStage | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 11 | 1% | – | – | outside top 12 |
| 2-10 employees | 73 | 4% | 11 | 7% | IBM DataStage |
| 11-50 employees | 185 | 11% | 27 | 16% | IBM DataStage |
| 51-200 employees | 284 | 17% | 31 | 18% | even |
| 201-500 employees | 240 | 15% | 15 | 9% | Azure Data Factory |
| 501-1000 employees | 196 | 12% | 15 | 9% | Azure Data Factory |
| 1001-5000 employees | 301 | 18% | 27 | 16% | Azure Data Factory |
| 5001-10000 employees | 110 | 7% | 10 | 6% | even |
| 10,001+ employees | 255 | 15% | 32 | 19% | IBM DataStage |
| Segment | Azure Data Factory | share | IBM DataStage | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 674 | 41% | 101 | 60% | IBM DataStage |
| Business & finance services | 388 | 23% | 38 | 23% | even |
| Manufacturing & industry | 197 | 12% | 6 | 4% | Azure Data Factory |
| Commerce & trade | 57 | 3% | 3 | 2% | even |
| Utilities & infrastructure | 57 | 3% | – | – | outside top 12 |
| Health care | 54 | 3% | 4 | 2% | even |
| Public, community & associations | 46 | 3% | 2 | 1% | even |
| Property & construction | 45 | 3% | 1 | 1% | Azure Data Factory |
| Consumer services & leisure | 34 | 2% | 4 | 2% | even |
| Transport, logistics & rental | 31 | 2% | – | – | outside top 12 |
| Media & content | 25 | 2% | 2 | 1% | even |
| Automotive | 24 | 1% | 3 | 2% | even |
| Hospitality & travel | – | – | 2 | 1% | outside top 12 |
| Agriculture & forestry | – | – | 1 | 1% | outside top 12 |
| Segment | Azure Data Factory | share | IBM DataStage | share | Leans |
|---|---|---|---|---|---|
| United States | 930 | 56% | 103 | 61% | IBM DataStage |
| United Kingdom | 131 | 8% | – | – | outside top 12 |
| Germany | 77 | 5% | 3 | 2% | Azure Data Factory |
| Netherlands | 63 | 4% | – | – | outside top 12 |
| Canada | 60 | 4% | 4 | 2% | even |
| France | 53 | 3% | 13 | 8% | IBM DataStage |
| India | 45 | 3% | 7 | 4% | even |
| Australia | 30 | 2% | 3 | 2% | even |
| Belgium | 24 | 1% | – | – | outside top 12 |
| Sweden | 23 | 1% | – | – | outside top 12 |
| Poland | 22 | 1% | 3 | 2% | even |
| Denmark | 16 | 1% | 2 | 1% | even |
| Italy | – | – | 6 | 4% | outside top 12 |
| Spain | – | – | 3 | 2% | outside top 12 |
| Portugal | – | – | 3 | 2% | outside top 12 |
| Bulgaria | – | – | 2 | 1% | outside top 12 |
| Segment | Azure Data Factory | share | IBM DataStage | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 4,545 | 66% | 696 | 48% | Azure Data Factory |
| Software engineering | 847 | 12% | 333 | 23% | IBM DataStage |
| IT operations & business systems | 548 | 8% | 167 | 12% | IBM DataStage |
| Product, program & project management | 67 | 1% | 45 | 3% | IBM DataStage |
| Engineering (non-software) | 51 | 1% | 9 | 1% | even |
| Sales | 40 | 1% | 7 | 0% | even |
| General management & consulting | 33 | 0% | 2 | 0% | even |
| Legal, compliance & risk | 15 | 0% | – | – | outside top 12 |
| Customer service & success | 12 | 0% | – | – | outside top 12 |
| Finance & accounting | 11 | 0% | 4 | 0% | even |
| Quality, safety & environment | 10 | 0% | – | – | outside top 12 |
| Marketing & communications | 8 | 0% | – | – | outside top 12 |
| Logistics warehouse and transport | – | – | 8 | 1% | outside top 12 |
| Unclassified | – | – | 3 | 0% | outside top 12 |
| Manufacturing and production | – | – | 2 | 0% | outside top 12 |
| Skilled trades and field service | – | – | 2 | 0% | outside top 12 |
The companies behind each column
Companies using Azure Data Factory →
Companies using IBM DataStage →
etl elt transformation ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
IBM DataStage is named by 168 companies in job postings and Azure Data Factory by 1,655; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/azure-data-factory-vs-ibm-datastage)
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.
Azure Data Factory vs IBM DataStage: questions
Which is more used, Azure Data Factory or IBM DataStage?
By companies naming it in their own job postings, Azure Data Factory (1,655) over IBM DataStage (168). By website-and-DNS footprint, IBM DataStage (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Azure Data Factory?
33% of Azure Data Factory companies have 2 to 200 employees, against 41% for IBM DataStage. 40% of Azure Data Factory companies have over 1,000 employees, against 41% for IBM DataStage.
Do companies run both?
0 companies name both in their postings, usually Azure Data Factory in marketing and IBM DataStage 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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