HTTP File connector

Set up the HTTP File connector in Kaivo: authentication, configuration, the BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated About 2 hours ago

Kaivo is a fully managed data platform that syncs your HTTP File 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 HTTP File instead of moving it.

What is the HTTP File connector

The HTTP File connector brings your HTTP File data into a managed BigQuery warehouse, kept up to date automatically so you can analyse it alongside the rest of your data.

CategoryFiles & Databases
StatusGenerally available
AuthenticationAPI key or Username and password
SetupSelf-service

Getting started with the HTTP File connector

  1. Sign up for Kaivo and create a workspace.
  2. Connect your HTTP File account.
  3. Choose which tables to sync.
  4. Wait for the initial sync to finish.
  5. Query your data in BigQuery or your favourite AI or BI tool.

Authenticating HTTP File

The HTTP File connector supports 3 ways to connect. Pick the one that fits how you work.

None

Authenticate with your HTTP File credentials. You provide:

Basic Authentication

Connect with your HTTP File login. You provide:

FieldDescription
Username
Password

Bearer Token

Authenticate with your Token.

FieldDescription
Token

Configuring the HTTP File connector

When you set up the connector, you provide:

FieldDescription
Name

A name to identify the tables parsed from the file(s).

URLs

One or more URLs to download. Multiple files are combined together and should follow the same schema.

Delimiter

A one-character string used to separate fields. If no value is given, auto-detection is used instead. An explicit value is recommended for reliability.

Quote Character

A one-character string used to quote fields containing special characters.

Escape Character

A one-character string used to remove any special meaning from the following character.

Text Encoding

File character encoding, such as utf-8 or latin-1. Defaults to utf-8-sig, which allows an optional BOM (byte order marker).

JSON Path Prefix

A path inside the JSON structure, in which the data items can be found. Path components must be separated by periods. Use "item" to indicate an array item.

Multiple JSON Values

Allow multiple top-level JSON values. Required for NDJSON (Newline-Delimited JSON) or JSONL (JSON Lines) files.

Comments

Allow C-style // ... and /* ... */ comments. Required for JSONC (JSON with Comments) files.

Tables and columns synced from HTTP File

The available streams and columns mirror the tables and fields in your own database, so they are determined when the connection runs rather than listed here.

How the HTTP File sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where HTTP File 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

How long does the initial sync take for HTTP File?

It depends on how much history is in your HTTP File 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?

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 HTTP File'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 HTTP File, 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?

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 HTTP File 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 HTTP File connector in Kaivo and all of its synced data is deleted with it.

Common use cases for HTTP File data

Teams sync your data from HTTP File to BigQuery with Kaivo to build reports and dashboards, combine HTTP File data with other sources, and power AI tools, all without exporting spreadsheets or writing pipelines.

Use HTTP File data in your AI and BI tools

Once HTTP File 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 HTTP File data.

Claude

Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →

Power BI

Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →

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

The premium analytics standard, with native BigQuery integration. Setup guide →

Google Sheets

Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →

Excel

Connect via Power Query's BigQuery connector. Setup guide →

Metabase

Open-source BI tool with strong BigQuery support. Setup guide →

See our pricing page for HTTP File connector pricing and plan details.