Source freshness checks in dbt - Mini Project: Build & Apply

Source Freshness Checks with dbt

📖 Scenario: You work as a data analyst in a company that relies on daily data updates from various sources. To ensure your reports are accurate, you need to check how fresh the data is in your source tables.Using dbt, you will set up source freshness checks to monitor the last update time of your data sources.

🎯 Goal: Build a dbt project that defines sources and configures freshness checks to monitor the last update timestamps of source tables.

📋 What You'll Learn

Create a source definition for a table named orders in the raw schema

Add a freshness check configuration with warn_after and error_after thresholds

Run the freshness check and output the results

💡 Why This Matters

🌍 Real World

Data teams use source freshness checks to ensure their reports and models rely on up-to-date data, preventing decisions based on stale information.

💼 Career

Knowing how to configure and run source freshness checks is essential for data analysts and engineers to maintain data quality and trust in analytics pipelines.

1

Define the source for the orders table

Create a source named raw with a table called orders. The table has a freshness timestamp column named last_updated. Write this in a YAML file named sources.yml.

dbt

# Define the source 'raw' with table 'orders' and freshness column 'last_updated'
# Your code here

Hint

Use the sources key and define name, tables, and freshness with warn_after and error_after thresholds.

2

Configure freshness thresholds

In the sources.yml file, set the freshness check to warn if data is older than 24 hours and error if older than 48 hours. Use warn_after and error_after with count and period keys.

dbt

version: 2
sources:
  - name: raw
    tables:
      - name: orders
        freshness:
          # Set warn_after threshold
          # Set error_after threshold
        loaded_at_field: last_updated
# Your code here

Hint

Use warn_after and error_after with count and period to set thresholds.

3

Run the freshness check in dbt

Run the dbt command to check source freshness using dbt source freshness in your terminal.

dbt

# Run the freshness check command in your terminal
# Your code here

Hint

Use the command dbt source freshness to run freshness checks.

4

Display the freshness check results

After running dbt source freshness, print the output showing the freshness status of the orders source table.

dbt

# Print the freshness check output below
# Your code here

Hint

Look at the terminal output after running dbt source freshness to see the freshness status.

Practice

1. What is the main purpose of source freshness checks in dbt?

easy

A. To track how recent the data in your source tables is

B. To create new tables from raw data

C. To optimize SQL query performance

D. To schedule dbt runs automatically

Solution

1. Step 1: Understand the role of freshness checks

   Freshness checks monitor the age of data in source tables to ensure it is up-to-date.

2. Step 2: Compare options to the purpose

   Only To track how recent the data in your source tables is describes tracking data recency, which matches the purpose of freshness checks.

3. Final Answer:

   To track how recent the data in your source tables is -> Option A

4. Quick Check:

   Freshness checks = track data recency [OK]

Hint: Freshness checks measure data age, not table creation or scheduling [OK]

Common Mistakes:

• Confusing freshness checks with table creation
• Thinking freshness checks optimize queries
• Assuming freshness checks schedule runs

2. Which of the following is the correct way to set a freshness check with a warning threshold of 1 day and an error threshold of 2 days in dbt YAML?

easy

A. freshness:
warn_after: 1 day
error_after: 2 day

B. freshness:
warn_after: {count: 1, period: day}
error_after: {count: 2, period: day}

C. freshness:
warn_after: '1 day'
error_after: '2 days'

D. freshness:
warn_after: {count: 2, period: day}
error_after: {count: 1, period: day}

Solution

1. Step 1: Recall correct YAML syntax for freshness

   dbt expects warn_after and error_after as objects with count and period keys.

2. Step 2: Match options to syntax

   freshness: warn_after: {count: 1, period: day} error_after: {count: 2, period: day} correctly uses {count: X, period: day} format; others use incorrect formats or swap thresholds.

3. Final Answer:

   freshness: warn_after: {count: 1, period: day} error_after: {count: 2, period: day} -> Option B

4. Quick Check:

   Use count and period keys in YAML freshness [OK]

Hint: Use {count: X, period: day} format for freshness thresholds [OK]

Common Mistakes:

• Using strings instead of objects for thresholds
• Swapping warn_after and error_after values
• Missing count or period keys

3. Given this freshness check result output, what is the status if the last loaded timestamp is 3 days ago, warn_after is 1 day, and error_after is 2 days?

{"status": "", "max_loaded_at": "2024-04-20T00:00:00Z"}

medium

A. error

B. warn

C. pass

D. unknown

Solution

1. Step 1: Calculate data age from last loaded timestamp

   If today is 2024-04-23, data is 3 days old (2024-04-23 - 2024-04-20).

2. Step 2: Compare data age to thresholds

   3 days > error_after (2 days), so status is error.

3. Final Answer:

   error -> Option A

4. Quick Check:

   Data age > error_after = error status [OK]

Hint: If data age > error_after, status is error [OK]

Common Mistakes:

• Confusing warn_after and error_after thresholds
• Assuming status is warn for data older than error_after
• Ignoring current date when calculating age

4. You wrote this freshness check YAML but it fails to run:

sources:
  - name: my_source
    freshness:
      warn_after: {count: 1, period: day}
      error_after: {count: 2, period: days}

What is the likely cause of the error?

medium

A. The count values must be strings, not numbers

B. Missing quotes around the period values

C. warn_after and error_after keys are swapped

D. The period value 'days' should be singular 'day'

Solution

1. Step 1: Check period values in freshness YAML

   dbt expects period values as singular strings like 'day', not plural 'days'.

2. Step 2: Identify error cause

   Using 'days' causes a validation error; changing to 'day' fixes it.

3. Final Answer:

   The period value 'days' should be singular 'day' -> Option D

4. Quick Check:

   Period values must be singular like 'day' [OK]

Hint: Use singular period names like 'day', not 'days' [OK]

Common Mistakes:

• Using plural period names
• Swapping warn_after and error_after
• Adding unnecessary quotes around numbers

5. You want to set up a freshness check for a source table that updates hourly. You want to warn if data is older than 2 hours and error if older than 4 hours. Which YAML snippet correctly sets this up?

hard

A. freshness:
warn_after: {count: '2', period: hour}
error_after: {count: '4', period: hour}

B. freshness:
warn_after: {count: 2, period: hours}
error_after: {count: 4, period: hours}

C. freshness:
warn_after: {count: 2, period: hour}
error_after: {count: 4, period: hour}

D. freshness:
warn_after: {count: 4, period: hour}
error_after: {count: 2, period: hour}

Solution

1. Step 1: Identify correct period and count values

   Period should be singular 'hour', counts are numbers without quotes.

2. Step 2: Check warn_after and error_after order

   warn_after must be less than error_after; 2 < 4 is correct.

3. Step 3: Validate options

   freshness: warn_after: {count: 2, period: hour} error_after: {count: 4, period: hour} matches correct syntax and logic; A uses strings for counts, B uses plural 'hours', D swaps thresholds.

4. Final Answer:

   freshness: warn_after: {count: 2, period: hour} error_after: {count: 4, period: hour} -> Option C

5. Quick Check:

   Use singular period and correct threshold order [OK]

Hint: Use singular period and warn_after < error_after [OK]

Common Mistakes:

• Using plural period names like 'hours'
• Putting counts as strings instead of numbers
• Swapping warn_after and error_after values