Create stream and write data

A guide to creating a stream and writing data to it

In this quick-start, we create a stream in Beneath and write some data to it using Python.

Install the Beneath SDK

If you haven’t already, follow the Install the Beneath SDK quick start to install and authenticate Beneath on your local computer.

Create a Beneath project

On Beneath, every data stream lives in a project. You can create many projects, and a project can contain many streams. You can think of projects as Git repositories.

To create a project from the command-line, run the following command:

beneath project create USERNAME/NEW_PROJECT_NAME

Replace USERNAME with your Beneath username or organization, and NEW_PROJECT_NAME with your project name.

If you prefer, you can also create a project straight from the web console using the Create project page.

Create a stream

In this example, we will create a stream of movies using Python. If you prefer, you can also create a stream using the CLI or from the web console using Create stream.

In Beneath, every stream has a schema that defines its fields and their data types, and a key consisting of one or more fields that uniquely identify a record. Schemas are based on GraphQL, and you can read more about them here.

We suggest you run the code in a Jupyter notebook, but you can use any Python environment you prefer:

import beneath
client = beneath.Client()
await client.start()

stream = await client.create_stream("USERNAME/PROJECT_NAME/movies", schema="""
    " A stream of movies "
    type Movie @schema {
        title: String! @key
        released_on: Timestamp! @key
        director: String
        budget_usd: Int
        rating: Float
""", update_if_exists=True)

# Stop the client at the end of your script
# await client.stop()

To run the above example, you need to:

  1. Replace the stream path with your username and project name
  2. Run the code in a context that supports await (asyncio). Notebooks support these by default, but if you’re using plain Python, you need to wrap it in an async function.
  3. Optionally adapt the schema to suit your use case

That’s it! You just created a stream on Beneath.

Write data

Now, let’s write some data to the stream.

First, navigate to your stream in the Beneath web console, where the URL will look like Keep this window open, so you can see the records flow as you write data.

Then run the following Python code, which writes a single record to the stream:

from datetime import datetime
await stream.write({
    "title":       "Star Wars: Episode IV",
    "released_on": datetime(year=1977, month=5, day=25),
    "director":    "George Lucas",
    "budget_usd":  11000000,
    "rating":      8.6,

Did you see it tick in? Try tweaking and running the code several times to write multiple records.

Writing more data

Let’s write some more data to our stream. We’ll fetch a dataset of movies from Github and write some random movies to our stream.

Here’s the Python code:

import aiohttp
import asyncio
from datetime import datetime
import random

# load a dataset of movies
url = ""
async with aiohttp.ClientSession() as session:
    async with session.get(url) as res:
        movies = await res.json(content_type=None)

# write a 100 random movies one-by-one
n = 100
for i in range(n):
    # get a random movie
    movie = random.choice(movies)

    # transform and write the movie
    await stream.write({
        "title":       str(movie["Title"]),
        "released_on": datetime.strptime(movie["Release Date"], "%b %d %Y"),
        "director":    movie["Director"],
        "budget_usd":  movie["Production Budget"],
        "rating":      movie["IMDB Rating"],

    # wait a while
    await asyncio.sleep(5)

Clean up

If you’d like to clean up, you can delete the stream and project you created in this tutorial. To delete the movies stream from the command-line:

beneath stream delete USERNAME/PROJECT_NAME/movies

Then you can delete the empty project:

beneath project delete USERNAME/PROJECT_NAME

More info

Under the “API” tab on the stream’s page in the web console, you will find several more guides for reading and writing to the stream.

Other examples of the Python SDK in action can be found in the examples repo. For full details about available classes and functions, check out the Python client API reference.