Our team of expert data engineers specializes in Dagster, Airflow, PySpark, and cloud platforms (AWS, GCP, Azure). Combined with DagUI's AI-powered acceleration, we deliver production-ready data pipelines faster than traditional consulting approaches.
Our expert data engineers combine deep technical expertise in modern data orchestration tools and cloud platforms with DagUI's AI-powered code generation. This unique combination enables us to deliver enterprise-grade data pipelines in days instead of weeks, without compromising on quality or best practices.
Certified experts in Dagster, Airflow, PySpark, and cloud platforms with years of production experience building scalable data infrastructure.
DagUI generates production-ready code using GenAI, reducing development time by 80% while maintaining enterprise-grade quality standards.
Deliver production-ready pipelines in days, not weeks. Our proven methodology and AI tools accelerate every phase of development.
Our team has extensive production experience with the industry's leading data orchestration and processing technologies, enabling us to deliver robust, scalable solutions rapidly.
Production-Ready Data Orchestration: Our experts design and implement Dagster pipelines that provide observability, testing, and asset management out of the box. We leverage Dagster's software-defined assets and declarative scheduling to build maintainable, testable data pipelines.
What We Deliver:
Delivery Time: Production-ready Dagster pipelines in 3-5 days with DagUI acceleration
Enterprise-Grade Workflow Orchestration: We build scalable Airflow DAGs using best practices, including dynamic DAG generation, custom operators, and robust error handling. Our team specializes in both Airflow 2.x and managed services like Google Cloud Composer, AWS MWAA, and Astronomer.
What We Deliver:
Delivery Time: Production-ready Airflow DAGs in 4-7 days with DagUI acceleration
Large-Scale Data Processing: Our PySpark experts build high-performance data transformation pipelines that process terabytes of data efficiently. We optimize Spark jobs for performance, cost, and reliability across cloud platforms.
What We Deliver:
Delivery Time: Production-ready PySpark pipelines in 5-8 days with DagUI acceleration
Our team has deep expertise across AWS, Google Cloud Platform, and Microsoft Azure, enabling us to deliver cloud-native data solutions that leverage each platform's unique strengths.
Services We Leverage:
Expertise: AWS Certified Solutions Architects with production experience building petabyte-scale data platforms
Services We Leverage:
Expertise: Google Cloud Professional Data Engineers with expertise in serverless data pipelines
Services We Leverage:
Expertise: Azure-certified data engineers with experience in enterprise data platforms
Our rapid delivery methodology combines expert engineering skills with DagUI's AI-powered code generation, enabling us to deliver production-ready data pipelines in days instead of weeks.
Our experts quickly understand your data sources, requirements, and constraints. Using proven frameworks and DagUI's intelligent analysis, we design optimal pipeline architectures in hours.
Time: 1-2 days
DagUI generates production-ready pipeline code using GenAI, dramatically reducing development time. Our experts review, optimize, and enhance the generated code to ensure it meets enterprise standards.
Time: 2-3 days (vs 2-3 weeks traditional)
Our cloud experts rapidly provision and configure infrastructure on AWS, GCP, or Azure using Infrastructure-as-Code (Terraform, CloudFormation, or ARM templates).
Time: 1 day (parallel with development)
Comprehensive testing ensures your pipelines are production-ready. We implement unit tests, integration tests, and data quality checks using industry-standard frameworks.
Time: 1 day
We deploy your pipelines to production with automated CI/CD and set up comprehensive monitoring, alerting, and observability dashboards.
Time: 1 day
We ensure your team can maintain and extend the pipelines. Knowledge transfer sessions, documentation, and ongoing support ensure long-term success.
Time: Ongoing
5-8 Days for Production-Ready Pipelines
vs. 4-8 weeks with traditional consulting approaches
End-to-end development of data pipelines using Dagster, Airflow, or PySpark, from design to production deployment.
Includes: Architecture design, code development, testing, deployment, and documentation
Migrate existing on-premises or legacy data pipelines to modern cloud platforms (AWS, GCP, Azure) with minimal downtime.
Includes: Assessment, migration planning, execution, and validation
Optimize existing pipelines for better performance, lower costs, and improved reliability. We analyze bottlenecks and implement optimizations.
Includes: Performance analysis, optimization recommendations, and implementation
Implement data quality frameworks, validation checks, and governance policies to ensure reliable, trustworthy data.
Includes: Data quality framework setup, validation rules, and monitoring
Ongoing management and maintenance of your data pipelines, ensuring they run smoothly and adapt to changing requirements.
Includes: 24/7 monitoring, incident response, and regular optimization
Train your team on modern data engineering tools and best practices, enabling them to build and maintain pipelines independently.
Includes: Hands-on workshops, documentation, and ongoing support
We offer expert services in data engineering and full-stack web & mobile development to help you build scalable, production-ready solutions.
Expert data engineering services with Dagster, Airflow, PySpark, and cloud platforms (AWS, GCP, Azure). Our team delivers production-ready data pipelines rapidly using DagUI's AI-powered acceleration.
Full-stack web and mobile application development services. Build scalable, modern applications with React, Node.js, React Native, and cloud-native architectures.
We needed to migrate 50+ legacy ETL pipelines from on-premises to AWS and modernize them. WordJog's team delivered production-ready pipelines in just 6 days using DagUI. The quality was exceptional, and the team was incredibly knowledgeable about AWS services.