In the realm of DataOps, efficiency and automation are paramount. One crucial aspect of managing data warehouses is scaling them appropriately based on workload demands. Typically, businesses experience variations in data processing requirements, with weekends often seeing reduced activity compared to weekdays. Addressing this, we'll delve into a solution that utilizes Python scripts and environment variables to dynamically adjust warehouse sizes, optimizing resource utilization and cost-effectiveness.

Understanding the Use Case:

Imagine you're tasked with managing data warehouses for a company's data operations. Your objective is to scale down warehouse sizes during weekends to conserve resources and minimise costs. To accomplish this, you've developed a Python script that determines whether it's a weekend or weekday, setting an environmental variable accordingly.

Implementation:

The solution involves integrating the Python script into the CI/CD pipeline and leveraging environment variables to adjust warehouse configurations dynamically.

• CI/CD Pipeline Integration:

  Within the CI pipeline configuration file (-ci.yml), a before_script section is added to the SOLE job. This section executes the Python script (determine_if_weekend.py) to determine if it's a weekend or weekday and sets the is_weekend environmental variable accordingly.

Set Up Snowflake:
  before_script:
    - eval `python $CI_PROJECT_DIR/scripts/determine_if_weekend.py`
    - echo $is_weekend

• Python Script:

The Python script (determine_if_weekend.py) employs the datetime module to ascertain the current day and time, considering the time zone (in this case, Europe/London). Based on the current day and time, it determines whether it's a weekend and exports the result as the is_weekend environmental variable.

#script determining if its weekend or not
from datetime import datetime
import pytz

tz_London = pytz.timezone('Europe/London')
d = datetime.now(tz_London)
x = d.isoweekday()
y = d.strftime("%H:%M:%S")

if (x == 5 and y >= '22:00:00') or (x >= 6 and y >= '00:00:00') or (x == 1 and y <= '06:00:00'):
    print("export is_weekend='true'")
else:
    print("export is_weekend='false'")

• Warehouse Configuration:

In the warehouses.template.yml file, a conditional statement is added within the INGESTION section to adjust the warehouse size based on the is_weekend environmental variable. During weekends, a smaller warehouse size is allocated, while a medium size is used on weekdays.

INGESTION:
  comment: Warehouse for Ingestion operations
  {% if env.is_weekend == 'true' %}   
  warehouse_size: Small 
  {% else %}    
  warehouse_size: Medium 
  {% endif %}
  auto_suspend: 60
  auto_resume: true
  namespacing: prefix
  environment: "{{ env.DATAOPS_ENV_NAME_PROD }}"

• Benefits:

  • Cost Optimization: By automatically scaling down warehouse sizes during weekends (or after end of working hours), unnecessary resource consumption is avoided, resulting in cost savings.

  • Resource Efficiency: Resources are allocated based on actual workload demands, ensuring optimal performance and efficiency.

  • Automation: The solution automates the process of adjusting warehouse configurations, reducing manual intervention and potential errors.

  • Scalability: The approach is scalable and can be adapted to accommodate different scheduling and workload patterns.

  • Maintainability: By encapsulating logic within scripts and environment variables, the solution remains modular and easy to maintain.

• Conclusion:

In the dynamic landscape of data operations, efficient resource management is essential. By leveraging Python scripts and environment variables within CI pipelines, businesses can dynamically adjust warehouse configurations, optimising resource utilisation and cost-effectiveness. This not only streamlines operations but also enhances scalability and maintainability, ensuring robust data infrastructure tailored to evolving business needs.