Streamline your data pipelines to support real-time decision-making capabilities. Deploy best practices across your Big Data stack.
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Prototype Development
An iterative approach through quick proof-of-concepts will help validate your Big Data innovations faster than a waterfall approach.
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Repeatable Deployments
Go from Jupyter Notebooks to Cloud-native containers. Automate the delivery of your pipeline using Continuous Integration/ Continuous Deployment (CI/CD). Prevent drift in your architecture using Infrastructure as Code (IaC) tools like Terraform.
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Observability
Improve your ability to troubleshoot issues, and find performance bottlenecks by adding instrumentation in your ETL process. Roll up the data into dashboards for real-time decision making.
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Data Pipeline Optimization
End-to-End Quality
Professional methods and tools to ensure robustness, dependability, functional safety, cybersecurity and usability:
- Agile process
- CI/CD
- DevOps pipelines
- Secrets management
- Source control management
- Automated testing
- Documentation
- Instrumentation
- Monitoring
Technology Expertise
20+ years of software development and deployment experience with a focus on:
- Python / Django / NodeJS
- AWS / GCP / Azure
- Databricks / Airflow
- React / Angular
- PostgreSQL
- scikit-learn
- Kubernetes / Terraform
- Linux / FreeBSD
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