This tutorial will add a few common tests to our data set.
To read more about Elementary’s suite of tests click here.
There are 3 models in our tutorial:
Customers
Orders
Returned Orders
In Elementary Data, tests are added to the schema.yml file. We will now navigate there and add tests for each of our models.
A schema.yml file that includes all the below tests can be found at the bottom of this page, so there is no need to manually add each test.
Test 1: Customer Signups
First, we will use the tables_anomalies test to perform a row_count. This tests counts the number of rows created in a given period to determine if there have been any anomalies in the number of signups in a given time period.
We will add the volume_anomalies test as follows:
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models: - name: customers description: This table has basic information about a customer, as well as some derived facts based on a customer's orders config: tags: ["PII"] tests: - elementary.volume_anomalies
Now that we have configured a test, we should define a column to use for our timestamp. This will be used to create time buckets for anomaly detection. We select the signup_date column as seen below:
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models: - name: customers description: This table has basic information about a customer, as well as some derived facts based on a customer's orders config: tags: ["PII"] elementary: timestamp_column: "signup_date" tests: - elementary.volume_anomalies
This test will fail if there are any days (as defined by signup_date) where the number of rows exceeds 3 standard deviations above/below the mean.
Test 2: Returned Orders
Similar to Test 1, we will use the volume_anomalies test to detect an anomalous number of returned orders in a given time period. In this test, however, we will define the timestamp column at the test level - instead of at the model level.
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- name: returned_orders description: This table contains all of the returned orders config: tags: ["finance"] tests: - elementary.volume_anomalies: tags: ["table_anomalies"] timestamp_column: "order_date"
Test 3: Dimension Anomalies in the Orders Model
The elementary.dimension_anomalies tests can be used to check for anomalies in the distribution of data in low cardinality fields. First, we will select the status column in the Orders model to run this test on:
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- name: orders description: This table has basic information about orders, as well as some derived facts based on payments config: tags: ["finance"] tests: - elementary.dimension_anomalies: dimensions: - status
Next, we will define a timestamp column for determining time buckets:
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- name: orders description: This table has basic information about orders, as well as some derived facts based on payments config: tags: ["finance"] elementary: timestamp_column: "order_date" tests: - elementary.dimension_anomalies: dimensions: - status
This test will gather row count metrics for each value in the status column and will fail if the distribution deviates substantially from the mean.
Test 4: Column Anomalies in the Customers Model
We will use the elementary.column_anomalies test to monitor the count of customers with 0 orders. To do so, we will define a zero_count test under elementary.column_anomalies for the number_of_orders column:
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- name: number_of_orders description: Count of the number of orders a customer has placed tests: - elementary.column_anomalies: config: severity: warn tags: ["column_anomalies"] column_anomalies: - zero_count timestamp_column: "signup_date"
Notice that we already defined the timestamp_column at the model level, so we don’t have to define it in the test. This page has in-depth details on test priorities.
Copy the text below into your schema.yml file to add all of the above tests to our project:
Final schema.yml file
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version: 2models: - name: customers description: This table has basic information about a customer, as well as some derived facts based on a customer's orders config: tags: ["PII"] elementary: timestamp_column: "signup_date" tests: - elementary.volume_anomalies columns: - name: customer_id description: This is a unique identifier for a customer - name: first_name description: Customer's first name. PII. - name: last_name description: Customer's last name. PII. - name: first_order description: Date (UTC) of a customer's first order - name: most_recent_order description: Date (UTC) of a customer's most recent order - name: number_of_orders description: Count of the number of orders a customer has placed tests: - elementary.column_anomalies: config: severity: warn tags: ["column_anomalies"] column_anomalies: - zero_count timestamp_column: "signup_date" - name: customer_lifetime_value description: Total value (AUD) of a customer's orders - name: customer_email description: Customer's email. PII. - name: signup_date description: Date (UTC) of a customer's signup to the online shop. - name: orders description: This table has basic information about orders, as well as some derived facts based on payments config: tags: ["finance"] elementary: timestamp_column: "order_date" tests: - elementary.dimension_anomalies: dimensions: - status columns: - name: order_id description: This is a unique identifier for an order - name: customer_id description: Foreign key to the customers table - name: order_date description: Date (UTC) that the order was placed - name: status description: '{{ doc("orders_status") }}' - name: amount description: Total amount (AUD) of the order - name: credit_card_amount description: Amount of the order (AUD) paid for by credit card - name: coupon_amount description: Amount of the order (AUD) paid for by coupon - name: bank_transfer_amount description: Amount of the order (AUD) paid for by bank transfer - name: gift_card_amount description: Amount of the order (AUD) paid for by gift card - name: returned_orders description: This table contains all of the returned orders config: tags: ["finance"] tests: - elementary.volume_anomalies: tags: ["table_anomalies"] timestamp_column: "order_date" columns: - name: order_id description: This is a unique identifier for an order - name: customer_id description: Foreign key to the customers table - name: order_date description: Date (UTC) that the order was placed - name: status description: '{{ doc("orders_status") }}' - name: amount description: Total amount (AUD) of the order - name: credit_card_amount description: Amount of the order (AUD) paid for by credit card - name: coupon_amount description: Amount of the order (AUD) paid for by coupon - name: bank_transfer_amount description: Amount of the order (AUD) paid for by bank transfer - name: gift_card_amount description: Amount of the order (AUD) paid for by gift card
You have now added Elementary tests to your project. Now let’s see how they are run and visualized.
