PRAYAG VERMA

My expertise centers around building scalable data pipelines, data warehousing solutions, and ETL/ELT processes. I specialize in cloud platforms like Azure and AWS, with strong skills in Python, SQL, Spark, and modern data tools like Databricks, ADF, and Airflow. I'm particularly strong in designing data architectures that balance performance, cost, and maintainability.

I believe data quality is the foundation of any successful data initiative. My approach includes implementing robust validation rules, automated testing pipelines, and monitoring systems to catch issues early. For governance, I work to establish clear data ownership, lineage tracking, and documentation practices. I've developed custom data quality frameworks that integrate with ETL processes to ensure consistency and reliability throughout the data lifecycle.

I've designed and implemented several real-time data processing systems using technologies like Apache Kafka, Azure Event Hubs, and Stream Analytics. One notable project involved creating a real-time customer analytics platform that processed millions of events per hour with sub-second latency. This system used a combination of stream processing for immediate insights and batch processing for historical analysis, providing business users with both real-time dashboards and comprehensive reporting capabilities.

Large-scale data migrations require careful planning and execution. My approach involves thorough source system analysis, detailed mapping documentation, and creating a robust testing strategy before any migration begins. I typically implement the migration in phases, starting with a proof of concept followed by incremental migrations when possible. Throughout the process, I use automated validation to verify data integrity and completeness. I also design fallback mechanisms and maintain parallel systems during the transition period to minimize business disruption.

I welcome guest contributions on topics related to data engineering, cloud technologies, analytics, or software development! To contribute, simply reach out through the contact form with the subject "Blog Contribution" and include a brief outline of your proposed topic. The ideal length is 1000-2000 words, and I encourage practical, hands-on content that provides value to readers. You'll receive full attribution for your work, and it's a great way to share your knowledge with the community while gaining exposure for your expertise.

The blog welcomes a wide range of technical topics, including but not limited to:

• Data engineering best practices and patterns
• Cloud architecture and implementation guides
• ETL/ELT techniques and tools
• Performance optimization for data pipelines
• Big data technologies and frameworks
• Data modeling and warehouse design
• Data quality and testing strategies
• Python, SQL, or Spark tutorials
• Real-world case studies and problem solving
• Emerging technologies in the data space

The most valuable contributions share practical insights, provide code examples when relevant, and offer actionable takeaways for readers.

I particularly enjoy working with Azure Databricks, Python, and modern data pipeline orchestration tools like Apache Airflow. I find Databricks especially powerful for its unified analytics platform that combines the best of data engineering and data science capabilities. On the AWS side, I'm excited about the capabilities of services like Glue, Redshift, and Step Functions for building serverless data workflows. I'm also increasingly interested in the intersection of data engineering with MLOps, and how we can build better pipelines to support model training and deployment.

Yes, I'm selectively available for freelance consulting on data engineering projects, particularly those involving complex data architectures, performance optimization, or cloud migrations. I can provide services ranging from architecture review and technical guidance to hands-on implementation and team mentoring. If you have a project in mind, please reach out through the contact form with details about your needs and timeline, and we can discuss how I might be able to help.