Nebius connector
Set up the Nebius connector in Kaivo: authentication, configuration, the 5 BigQuery tables it syncs, and answers to common questions.
Nebius connector pricing, tier and credits on kaivo.io
Kaivo is a fully managed data platform that syncs your Nebius data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your data from Nebius instead of moving it.
What is the Nebius connector
Section titled “What is the Nebius connector”Sync your Nebius AI models, files, and batch jobs into BigQuery with Kaivo to track usage and outcomes across your AI workloads.
| Category | Tech |
|---|---|
| Authentication | API key |
| Setup | Self-service |
Getting started with the Nebius connector
Section titled “Getting started with the Nebius connector”- Sign up for Kaivo and create a workspace.
- Connect your Nebius account.
- Choose which tables to sync.
- Wait for the initial sync to finish.
- Query your data in BigQuery or your favourite AI or BI tool.
Authenticating Nebius
Section titled “Authenticating Nebius”Authenticate with your API Key.
| Field | Description |
|---|---|
| API Key | |
| Your Nebius AI API key. |
|
Configuring the Nebius connector
Section titled “Configuring the Nebius connector”When you set up the connector, you provide:
| Field | Description |
|---|---|
| Start Date | |
| Any data before this date will not be fetched. |
|
Tables and columns synced from Nebius
Section titled “Tables and columns synced from Nebius”Kaivo syncs 5 tables from Nebius into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.
batch_results (13 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
completion_window |
STRING |
|
created_at |
FLOAT64 |
|
endpoint |
STRING |
|
error_file_id |
STRING |
|
id |
STRING |
|
input_file_id |
STRING |
|
object |
STRING |
|
request_counts__completed |
FLOAT64 |
|
request_counts__failed |
FLOAT64 |
|
request_counts__total |
FLOAT64 |
|
status |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
batches (13 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
completion_window |
STRING |
|
created_at |
FLOAT64 |
|
endpoint |
STRING |
|
error_file_id |
STRING |
|
id |
STRING |
|
input_file_id |
STRING |
|
object |
STRING |
|
request_counts__completed |
FLOAT64 |
|
request_counts__failed |
FLOAT64 |
|
request_counts__total |
FLOAT64 |
|
status |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
file_contents (12 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
glossary__gloss_div__gloss_list__gloss_entry__abbrev |
STRING |
|
glossary__gloss_div__gloss_list__gloss_entry__acronym |
STRING |
|
glossary__gloss_div__gloss_list__gloss_entry__gloss_def__para |
STRING |
|
glossary__gloss_div__gloss_list__gloss_entry__gloss_see |
STRING |
|
glossary__gloss_div__gloss_list__gloss_entry__gloss_term |
STRING |
|
glossary__gloss_div__gloss_list__gloss_entry__id |
STRING |
|
glossary__gloss_div__gloss_list__gloss_entry__sort_as |
STRING |
|
glossary__gloss_div__title |
STRING |
|
glossary__title |
STRING |
|
uuid |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
Subtable: file_contents__glossary__gloss_div__gloss_list__gloss_entry__gloss_def__gloss_see_also
Section titled “Subtable: file_contents__glossary__gloss_div__gloss_list__gloss_entry__gloss_def__gloss_see_also”| Column | Type | Description |
|---|---|---|
_kaivo_parent_id |
STRING |
Foreign key referencing _kaivo_id in the parent table. Auto-generated by Kaivo. |
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
value |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
files (8 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
bytes |
FLOAT64 |
|
created_at |
FLOAT64 |
|
filename |
STRING |
|
id |
STRING |
|
object |
STRING |
|
purpose |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
models (6 columns)
| Column | Type | Description |
|---|---|---|
_kaivo_id |
STRING |
Primary key that uniquely identifies the row. Auto-generated by Kaivo. |
created |
FLOAT64 |
|
id |
STRING |
|
object |
STRING |
|
owned_by |
STRING |
|
_kaivo_extracted_at |
TIMESTAMP |
Timestamp that shows when the row was extracted. Auto-generated by Kaivo. |
How the Nebius sync works
Section titled “How the Nebius sync works”After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Nebius supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won’t get duplicate rows.
Frequently asked questions
Section titled “Frequently asked questions”How long does the initial sync take for Nebius?
Section titled “How long does the initial sync take for Nebius?”It depends on how much history is in your Nebius account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they’re much faster.
Can I sync only some tables or columns?
Section titled “Can I sync only some tables or columns?”Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don’t select are never copied to your warehouse.
What happens when Nebius’s schema changes?
Section titled “What happens when Nebius’s schema changes?”New fields are never added automatically. You choose which fields to sync, so data you haven’t selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table’s sync mode: full-refresh tables always match what’s currently in Nebius, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.
How do I handle GDPR or data deletion requests?
Section titled “How do I handle GDPR or data deletion requests?”Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Nebius instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Nebius connector in Kaivo and all of its synced data is deleted with it.
Common use cases for Nebius data
Section titled “Common use cases for Nebius data”Batch tracking
Section titled “Batch tracking”Use batches with request_counts and status to monitor batch job progress and results.
Model inventory
Section titled “Model inventory”Report on models to see what is available and in use.
File management
Section titled “File management”Use files with bytes and purpose to track uploaded data and its size.
Job outcomes
Section titled “Job outcomes”Join batches with batch_results to analyse completion and errors.
Use Nebius data in your AI and BI tools
Section titled “Use Nebius data in your AI and BI tools”Once Nebius data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here’s how the most common destinations work with Nebius data.
Claude
Section titled “Claude”Use Kaivo’s MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →
Power BI
Section titled “Power BI”Microsoft’s BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →
Data Studio
Section titled “Data Studio”Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →
Tableau
Section titled “Tableau”The premium analytics standard, with native BigQuery integration. Setup guide →
Google Sheets
Section titled “Google Sheets”Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →
Connect via Power Query’s BigQuery connector. Setup guide →
Metabase
Section titled “Metabase”Open-source BI tool with strong BigQuery support. Setup guide →
See our pricing page for Nebius connector pricing and plan details.