Neutral
AI recommendation signal analysis across 79 domains for the Neutral persona in Data Warehouse Lakehouse Platforms.
Many tables and charts on this page show only the top few results; the full data behind them runs far deeper. The complete report unlocks every row, chart, and download for this industry.
Get the full Data Warehouse Lakehouse Platforms report →How to use this page
Place guest content
The sites surfacing for guest-post queries in this industry: a ready-made list of publications open to outside contributors.
Where the numbers come from
We run guest posts queries for this industry through Google and aggregate every result: domains by rank-weighted score (higher positions count for more) and appearance count, exact URLs by appearance count, and the most common title phrases.
What's on this page
Domain and URL charts, the full result list, and title n-gram tables.
Research: Guest Posts
Domains appearing in Google results for Neutral's Research: Guest Posts queries. Score is a rank-weighted sum (higher-ranked appearances count for more); count is a plain appearance tally.
Top URLs
Individual pages (not just domains) ranked by the same rank-weighted score, labeled by page title.
All Results
Every result for Neutral's Research: Guest Posts queries, ranked by how many times each exact URL appeared (ties broken by average rank position, so appearing higher up wins), a different aggregation than the score-based charts above. Title and URL links open in a new tab.
Title N-Grams
Most common word phrases (2 to 7 words) across every result title for these queries.
2-grams
| 26 | data warehouse |
| 23 | guest post |
| 12 | write for |
| 12 | for us |
| 11 | data warehousing |
| 5 | a data |
| 5 | data warehouses |
| 4 | how to |
| 4 | vs data |
| 4 | submit a |
| 4 | us guest |
| 4 | aws partner |
| 4 | the data |
| 3 | data mesh |
| 3 | in data |
| 3 | a guest |
| 3 | big data |
| 2 | is not |
| 2 | types of |
| 2 | i m |
| 2 | m datagor |
| 2 | datagor preset |
| 2 | preset s |
| 2 | s ai |
| 2 | ai data |
3-grams
| 12 | write for us |
| 4 | for us guest |
| 3 | a guest post |
| 3 | vs data warehouse |
| 2 | i m datagor |
| 2 | m datagor preset |
| 2 | datagor preset s |
| 2 | preset s ai |
| 2 | s ai data |
| 2 | ai data engineer |
| 2 | a data warehouse |
| 2 | data warehouses in |
| 2 | how to submit |
| 2 | to submit a |
| 2 | submit a post |
| 2 | us guest blogging |
| 2 | submit a guest |
| 2 | guest post for |
| 2 | aws partner guest |
| 2 | partner guest post |
| 2 | guest post aws |
| 2 | post aws partner |
| 2 | aws partner network |
| 2 | partner network apn |
| 2 | network apn blog |
4-grams
| 4 | write for us guest |
| 2 | i m datagor preset |
| 2 | m datagor preset s |
| 2 | datagor preset s ai |
| 2 | preset s ai data |
| 2 | s ai data engineer |
| 2 | how to submit a |
| 2 | for us guest blogging |
| 2 | submit a guest post |
| 2 | a guest post for |
| 2 | aws partner guest post |
| 2 | partner guest post aws |
| 2 | guest post aws partner |
| 2 | post aws partner network |
| 2 | aws partner network apn |
| 2 | partner network apn blog |
| 2 | what is a data |
| 1 | guest post when your |
| 1 | post when your dwh |
| 1 | when your dwh is |
| 1 | your dwh is not |
| 1 | dwh is not a |
| 1 | is not a dwh |
| 1 | not a dwh blog |
| 1 | data warehousing data mesh |
5-grams
| 2 | i m datagor preset s |
| 2 | m datagor preset s ai |
| 2 | datagor preset s ai data |
| 2 | preset s ai data engineer |
| 2 | write for us guest blogging |
| 2 | aws partner guest post aws |
| 2 | partner guest post aws partner |
| 2 | guest post aws partner network |
| 2 | post aws partner network apn |
| 2 | aws partner network apn blog |
| 1 | guest post when your dwh |
| 1 | post when your dwh is |
| 1 | when your dwh is not |
| 1 | your dwh is not a |
| 1 | dwh is not a dwh |
| 1 | is not a dwh blog |
| 1 | data warehousing data mesh different |
| 1 | warehousing data mesh different types |
| 1 | data mesh different types of |
| 1 | mesh different types of goals |
| 1 | guest post i m datagor |
| 1 | post i m datagor preset |
| 1 | guest post automating customer feature |
| 1 | post automating customer feature requests |
| 1 | automating customer feature requests and |
6-grams
| 2 | i m datagor preset s ai |
| 2 | m datagor preset s ai data |
| 2 | datagor preset s ai data engineer |
| 2 | aws partner guest post aws partner |
| 2 | partner guest post aws partner network |
| 2 | guest post aws partner network apn |
| 2 | post aws partner network apn blog |
| 1 | guest post when your dwh is |
| 1 | post when your dwh is not |
| 1 | when your dwh is not a |
| 1 | your dwh is not a dwh |
| 1 | dwh is not a dwh blog |
| 1 | data warehousing data mesh different types |
| 1 | warehousing data mesh different types of |
| 1 | data mesh different types of goals |
| 1 | guest post i m datagor preset |
| 1 | post i m datagor preset s |
| 1 | guest post automating customer feature requests |
| 1 | post automating customer feature requests and |
| 1 | how to create a data model |
| 1 | to create a data model for |
| 1 | create a data model for a |
| 1 | a data model for a data |
| 1 | data model for a data warehouse |
| 1 | guest post using lamini to train |
7-grams
| 2 | i m datagor preset s ai data |
| 2 | m datagor preset s ai data engineer |
| 2 | aws partner guest post aws partner network |
| 2 | partner guest post aws partner network apn |
| 2 | guest post aws partner network apn blog |
| 1 | guest post when your dwh is not |
| 1 | post when your dwh is not a |
| 1 | when your dwh is not a dwh |
| 1 | your dwh is not a dwh blog |
| 1 | data warehousing data mesh different types of |
| 1 | warehousing data mesh different types of goals |
| 1 | guest post i m datagor preset s |
| 1 | post i m datagor preset s ai |
| 1 | guest post automating customer feature requests and |
| 1 | how to create a data model for |
| 1 | to create a data model for a |
| 1 | create a data model for a data |
| 1 | a data model for a data warehouse |
| 1 | guest post using lamini to train your |
| 1 | post using lamini to train your own |
| 1 | using lamini to train your own llm |
| 1 | lamini to train your own llm on |
| 1 | reference for doing data warehousing in sql |
| 1 | for doing data warehousing in sql server |
| 1 | data warehouse implementation what to know before |