Don't hire DevOps Engineers for your cloud data pipelines.
Many DevOps engineers will hate me for saying these, but I believe, that having a background in data and from the experience of working with DevOps engineers trying to be data engineers, I definitely qualify to say what I am going to say. Data is the lifeblood of modern businesses, but building efficient data pipelines can be a challenge. I've observed a growing trend of DevOps engineers using CI/CD automation tools for data pipelines, while these tools excel in application development, they might not be the best fit for data engineering workflows.
Here's why:
Focus on Code vs. Data: DevOps tools are geared towards code deployments, neglecting the unique needs of data pipelines like data quality checks, schema validation, and lineage tracking.
Unnecessary Complexity: Applying DevOps practices directly to data pipelines can introduce unnecessary complexity, hindering maintainability and scalability. High reliability on the on-prem development infrastructure.
The Solution: Embrace Data-Centric Tools
Data engineering requires a specialized approach. Let's leverage purpose-built data orchestration tools that offer:
Native Data Handling: Seamless integration with data sources, transformation frameworks, and data warehouses for a smoother flow. Use tools such as Fivetran, Talend, Azure Datafactory, Airflow, VMs, DBT Cloud and Cloud Data Warehouses.
Built-in Monitoring & Lineage: Automated data quality checks and data lineage tracking for better pipeline reliability and debugging. Use ADF, Databricks, Synapse, AWS Glue, and so on.
Scalability for Big Data: Designed to handle massive data volumes efficiently.
By adopting data-centric automation, we can build robust, maintainable data pipelines that deliver timely, high-quality data for better decision-making.
What are your thoughts on data pipeline automation? Share your experiences in the comments!
#dataengineering #datapipelines #devops #cicd #automation #bigdata