Datasets on Ouro

Datasets store tabular data in SQL tables—perfect for sensor logs, transactions, experiment data, and more. They give you:

1. Schema control: define columns, types, and constraints.
2. SQL power: filter, join, and aggregate with familiar queries.
3. Easy integration: connect BI or visualization tools over SQL.
4. Shared source of truth: one place for teams to read and write data.
5. Scale: Ouro’s infrastructure handles large volumes efficiently.

Each dataset automatically exposes a REST API, generated from its schema, so you can query or update data programmatically right away.

Create a dataset

Choose the method that fits your workflow (web UI, API, or client library).

1. From a CSV file

When you upload a CSV file, Ouro will automatically convert it into a dataset. Ouro will automatically infer the schema of the dataset columns and data types.

• CSV → auto‑convert to a dataset.
• Non‑tabular files stay as regular file assets.
• JSON / Parquet support coming soon.

Using the Python SDK, you can read your data with Pandas DataFrames and upload it to Ouro.

import pandas as pd
 
df = pd.read_csv('path/to/my_file.csv')
dataset = ouro.datasets.create(data=df, name='my_dataset', visibility='public')

2. Provide a schema

For more control over the schema, you can provide a CREATE TABLE statement.

CREATE TABLE datasets.my_dataset (
  id   INTEGER PRIMARY KEY,
  name VARCHAR(255),
  age  INTEGER,
  email VARCHAR(255)
);

Visualize your data

Once you've added your data to Ouro, you will automatically get a visualization based on the structure of the dataset.

Query your data

Working with your data is just as easy as adding it.

df = ouro.datasets.query(dataset_id)

For larges datasets that can't all be loaded at once, we expose a SQL interface for fine-grained queries.

Datasets turn raw tables into living assets—queryable, shareable, and ready for analysis the moment they're created.