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Supports Http Connections

Build simple analytics apps with

Columns Ai is missioned to enable our users to analyze and visualize whatever data they see. Hence, supporting a wide range of data connections is a keep-going effort our builders will do.

We off started supporting Google spreadsheet and CSV data upload, now we have HTTP joined the growing list, public/unauth for now. Even further, thinking as a platform, it’s nice to introduce “template” concept, with template, transforms a data as an app!

Connect to a HTTP endpoint with supported formats (csv, json, …)

Now, let’s take a quick look at two examples to demonstrate how interesting this could be, the first example is connecting to a REST API returning json payload, the other one is connecting to a normal raw csv file hosted in Github.

Example 1: A simple app to analyze stock data

In this example, we use the REST api provided by, you can experience it now by going to this link: visualize a stock, by entering the stock symbol, it brings up the 2yr data points till query time on server for you to analyze and build visualizations from. A story could look like this:

Now, let’s see steps 1 → 2 →3 to create a data as an app:

  • Step 1
    Sign in and navigate to dashboard/data page, click +CREATE and choose “HTTP”, paste an example HTTP link:
Paste Http Url — detects payload for rows extraction, and columns mapping

Columns previews the payload samples and try to extract rows of data, it also list optional fields for user to define column map as shown in above image.

In this example, it found “results” in the payload contains all the rows, and for each row, we define columns as v →volume, o → open (price), c → close, h → high, l → low and t →time, the unchecked “vw” and “n” are ignored.

  • Step 2
    Make it dynamic by templating some segments in the URL to a user input or predefined macros — this is the way how you make your data source dynamic depending on final user’s input!
Turn on “make it dynamic” and try to build the URL template
  • Step 3
    Save the data source by choosing proper column types if necessary and set the TIME column (check its value to match selected time definition). And “import” it.
Continue from last step — just type a few metadata for this new data source and import

Now, this new data source should be available in your data collection, as shown like mine:

I shared this data source, it’s public now

I shared this data definition, that’s why you can access it from the visual link I shared earlier.

More open data, the world is more connected!

Example 2: import data of raw CSV from Github

The second example, let’s take a look at a Covid data set shared on Github by NYTIMES, this is regularly updated CSV dump at about 100MB, here is the homepage of this data.

Similarly as the first example, just paste the data link after clicked “HTTP” option in the data import page, it detects the data samples and data format as CSV.

CSV data has very basic properties to look at

Click “submit” and next step clicks “import” after filling the basic data source properties such as name, description.

And we’re ready to query it!

One way I looked at the data

total covid cases ranked by state in 2021

One example to look at this data hosted in Github

A bit more fancy — I can choose an animation to visualize it, such as:

total covid cases raced daily by states in 2021

build an animation Bar Race from fresh covid data hosted in Github provided by nytimes

See this animation story shared publicly on columns:


I hope you enjoyed these 2 examples demonstrated for how HTTP connection is supported.

By supporting HTTP connections, it unblocks many interesting scenarios, when we say Columns Ai is a growing flexible platform that our users can build extensive apps on top of it with unlimited power, this is one good step on track. Cheers!

At last but not least, we would like to hear from you, as builders, we want to build things meaningful to our users, so please get in touch at, cheers!




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Shawn Cao

Shawn Cao

Drive towards the mission of enabling data science technology accessible to everyone.

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