Load Your Data

Nothing in Keboola happens until there is data in Storage. This step puts four tables there using a data source connector — the thing that does all real loading in Keboola. Step 2 of the Getting Started arc.

What you need

A Keboola project you can sign in to. If you do not have one, start with Get a Project.

Nothing to download — the connector fetches the files itself.

The sample data

Four small tables describing a sales pipeline: opportunities, the accounts they belong to, the users who own them, and each user’s seniority level. They are published here, and the connector reads them over HTTPS:

Table	File
opportunities	/getting-started/opportunity.csv
accounts	/getting-started/account.csv
users	/getting-started/user.csv
levels	/getting-started/level.csv

All characters in this data are fictitious, and any resemblance to real persons, living, dead, undead, unborn, or otherwise semi-existent is purely coincidental.

How loading works

Data gets into Keboola through data source connectors — components that fetch from a source on demand or on a schedule. There are hundreds of them: databases, APIs, ad platforms, cloud drives. They differ in how they authenticate and what they can fetch, but they all end the same way: rows in a Storage table.

You will use the HTTP connector, which downloads CSV files from a URL. It is the simplest one that still behaves like a real connector — it can be re-run, scheduled, and dropped into a flow, which is what step 5 depends on.

One configuration can fetch many files, so you will build a single configuration holding four tables rather than four separate configurations.

Create the configuration

1. Open Components and search for HTTP. Pick the HTTP data source.

   

2. Click Add Component, then create a configuration named [TUTORIAL] Sample data and give it a description. Descriptions are what keep a project readable six months later; see the best practices cheat sheet.

   

3. Set the base URL — the prefix shared by every file this configuration downloads:

   https://help.keboola.com

   

Add the four tables

Each table is a configuration row: its own path and settings, sharing the configuration’s base URL.

4. Click Add Table and name it opportunity. The name seeds the Storage table name.

5. In the table’s settings, set the path to the file, relative to the base URL:

   /getting-started/opportunity.csv

6. Under the header options, choose Read from the file(s) header — the sample files carry column names on the first line. (The alternatives are setting the columns by hand, or having them generated as col_1, col_2, ….)

   

7. Save the table, then repeat steps 4–6 for the other three:

   Table name	Path
   account	/getting-started/account.csv
   user	/getting-started/user.csv
   level	/getting-started/level.csv

8. Run the configuration.

   

Check it worked

Open Storage. Data lives in buckets, and each bucket holds tables. The connector created a bucket of its own — named after the configuration, something like keboola-ex-http-1234567, shown with an IN badge — holding four tables: opportunity, account, user and level.

Click a table to see its columns, row count and a Data Sample of the real contents. If opportunity has a few hundred rows and columns like AccountId and Probability, this step is done.

Your bucket name will differ

The bucket name contains the configuration’s own ID, so yours will not match the screenshots exactly — and that is fine. The next step lets you name these tables whatever you like inside the transformation, and it is those names the SQL depends on.

If it goes wrong

• The job fails with a 404. The path is wrong, or missing its leading slash. Base URL and path are concatenated, so https://help.keboola.com + /getting-started/user.csv is right, while a base URL with a trailing slash gives you a double slash.
• Every row arrives as one column. The Delimiter setting does not match the file. These files are comma-separated.
• The first data row is missing, or columns are called col_1. Wrong header option — pick Read from the file(s) header.
• The table is empty but the job succeeded. The URL returned an HTML error page instead of a CSV. Open it in a browser to see what actually comes back.

Or ask Kai

Kai can inspect what landed and tell you whether it looks right:

List the tables the HTTP connector just created, with their row counts, and show me a sample of opportunity.

Going further

• Load from Google Sheets — the same data pulled from a spreadsheet, with an authorized account instead of a public URL.
• Load from a database — the pattern every database connector follows.

Next: Transform your data →