Google BigQuery VS Teradata
As of 30 October 2026, Teradata is named by 225 companies and Google BigQuery by 2,715; Google BigQuery skews to companies under 200 people (48% vs 38%), Teradata to companies over 1,000 (44% vs 26%).
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 BigQuery's script and mail records show on hundreds of thousands of small sites, Teradata rarely leaves a trace outside its own apps. Compare on the job-posting numbers; use the footprint for reach.
- Google BigQuery: 2–200 employees
- 48%
- Teradata: 2–200 employees
- 38%
- Google BigQuery: 1,000+ employees
- 26%
- Teradata: 1,000+ employees
- 44%
- Postings per company, 90 days
- 3.1 · 4.2
- 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 BigQuery | share | Teradata | share | Leans |
|---|---|---|---|---|---|
| Self-employed | 42 | 2% | 2 | 1% | even |
| 2-10 employees | 218 | 8% | 17 | 8% | even |
| 11-50 employees | 439 | 16% | 37 | 16% | even |
| 51-200 employees | 651 | 24% | 32 | 14% | Google BigQuery |
| 201-500 employees | 393 | 14% | 26 | 12% | Google BigQuery |
| 501-1000 employees | 257 | 9% | 13 | 6% | Google BigQuery |
| 1001-5000 employees | 361 | 13% | 30 | 13% | even |
| 5001-10000 employees | 102 | 4% | 11 | 5% | even |
| 10,001+ employees | 252 | 9% | 57 | 25% | Teradata |
| Segment | Google BigQuery | share | Teradata | share | Leans |
|---|---|---|---|---|---|
| Technology & software | 1,279 | 47% | 125 | 56% | Teradata |
| Business & finance services | 582 | 21% | 55 | 24% | Teradata |
| Media & content | 163 | 6% | 6 | 3% | Google BigQuery |
| Manufacturing & industry | 157 | 6% | 6 | 3% | Google BigQuery |
| Commerce & trade | 145 | 5% | 8 | 4% | even |
| Consumer services & leisure | 79 | 3% | 4 | 2% | even |
| Utilities & infrastructure | 62 | 2% | 6 | 3% | even |
| Health care | 60 | 2% | 2 | 1% | even |
| Public, community & associations | 45 | 2% | 2 | 1% | even |
| Property & construction | 42 | 2% | – | – | outside top 12 |
| Hospitality & travel | 40 | 1% | 5 | 2% | even |
| Transport, logistics & rental | 35 | 1% | 2 | 1% | even |
| Automotive | – | – | 3 | 1% | outside top 12 |
| Segment | Google BigQuery | share | Teradata | share | Leans |
|---|---|---|---|---|---|
| United States | 1,633 | 60% | 146 | 65% | Teradata |
| United Kingdom | 240 | 9% | 16 | 7% | even |
| Germany | 114 | 4% | 4 | 2% | Google BigQuery |
| France | 111 | 4% | 7 | 3% | even |
| India | 59 | 2% | 7 | 3% | even |
| Canada | 52 | 2% | 5 | 2% | even |
| Brazil | 50 | 2% | – | – | outside top 12 |
| Netherlands | 43 | 2% | 3 | 1% | even |
| Sweden | 43 | 2% | – | – | outside top 12 |
| Australia | 42 | 2% | 6 | 3% | even |
| Spain | 38 | 1% | 4 | 2% | even |
| Poland | 30 | 1% | 4 | 2% | even |
| Singapore | – | – | 5 | 2% | outside top 12 |
| Czechia | – | – | 2 | 1% | outside top 12 |
| Segment | Google BigQuery | share | Teradata | share | Leans |
|---|---|---|---|---|---|
| Data & AI | 5,837 | 53% | 956 | 51% | Google BigQuery |
| Software engineering | 1,678 | 15% | 218 | 12% | Google BigQuery |
| IT operations & business systems | 610 | 6% | 201 | 11% | Teradata |
| Marketing & communications | 389 | 4% | 14 | 1% | Google BigQuery |
| Sales | 294 | 3% | 47 | 2% | even |
| Product, program & project management | 258 | 2% | 35 | 2% | even |
| Finance & accounting | 175 | 2% | 19 | 1% | even |
| Logistics warehouse and transport | 134 | 1% | – | – | outside top 12 |
| Engineering (non-software) | 87 | 1% | 5 | 0% | even |
| Customer service & success | 74 | 1% | – | – | outside top 12 |
| Business development & strategy | 45 | 0% | – | – | outside top 12 |
| Legal, compliance & risk | 44 | 0% | 15 | 1% | even |
| General management & consulting | – | – | 15 | 1% | outside top 12 |
| Unclassified | – | – | 4 | 0% | outside top 12 |
| Human resources | – | – | 3 | 0% | outside top 12 |
The companies behind each column
Companies using Google BigQuery →
Companies using Teradata →
data warehouses olap ranked →
Quote the sentence with the date and the link. The numbers change daily; the link always shows the current ones.
Teradata is named by 225 companies in job postings and Google BigQuery by 2,715; 0 name both. (Breaknorm, 30 October 2026, https://breaknorm.co/compare/google-bigquery-vs-teradata)
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 BigQuery vs Teradata: questions
Which is more used, Google BigQuery or Teradata?
By companies naming it in their own job postings, Google BigQuery (2,715) over Teradata (225). By website-and-DNS footprint, Teradata (0 vs 0), because its scripts and mail records sit on many small company sites.
Who chooses Google BigQuery?
48% of Google BigQuery companies have 2 to 200 employees, against 38% for Teradata. 26% of Google BigQuery companies have over 1,000 employees, against 44% for Teradata.
Do companies run both?
0 companies name both in their postings, usually Google BigQuery in marketing and Teradata 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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