Review the Flow

See a screencast covering this section’s steps

One of the first concepts a user needs to understand about Dataiku is the Flow. The Flow is the visual representation of how datasets, recipes (steps for data transformation), and models work together to move data through an analytics pipeline.

See the Flow’s visual grammar

Dataiku has its own visual grammar to organize AI, machine learning, and analytics projects in a collaborative way.

Shape	Item	Icon
	Dataset	The icon on the square represents the dataset’s storage location, such as Amazon S3, Snowflake, PostgreSQL, etc.
	Recipe	The icon on the circle represents the type of data transformation, such as a broom for a Prepare recipe or coiled snakes for a Python recipe.
	Model	The icon on the diamond represents the type of modeling task, such as prediction, clustering, time series forecasting, etc.

Tip

In addition to shape, color has meaning too.

• Datasets are blue, but those shared from other projects are black.

• Visual recipes are yellow. Code recipes are orange. LLM recipes are pink. Plugin recipes are red.

• Machine learning elements are green.

Take a look now!

1. If not already there, from the left-most menu in the top navigation bar, click on the Flow (or use the keyboard shortcut g + f).

2. Double click on the job_postings dataset to open it.

Tip

There are many other keyboard shortcuts beyond g + f. Type ? to pull up a menu or see the complete list in the reference documentation.

Analyze the data

This project begins from a labeled dataset named job_postings composed of 95% real and 5% fake job postings. For the column fraudulent, values of 0 and 1 represent real and fake job postings, respectively. Your task will be to build a prediction model capable of classifying a job posting as real or fake.

Let’s take a quick look at the data.

1. Click on the header of the first column job_id to open a menu of options.

2. Select Analyze.

3. Use the arrows at the top left of the dialog to cycle through presentations of each column summary, including the target variable fraudulent column.

Build the Flow

Unlike the initial uploaded datasets, the downstream datasets appear as outlines. This is because they have not been built, meaning that the relevant recipes have not been run to populate these datasets. However, this is not a problem because the Flow contains the recipes required to create these outputs at any time.

1. Navigate back to the Flow (g + f).

2. Click to open the Flow Actions menu in the bottom right.

3. Click Build all.

4. Leaving the default options, click Build to run the recipes necessary to create the items furthest downstream.

5. When the job completes, refresh the page to see the built Flow.

Note

To learn more about creating this Flow, see the Quick Start | Dataiku for data preparation.