An iterative approach through quick proof-of-concepts will help validate your Big Data innovations faster than a waterfall approach.
Streamline your data pipelines to support real-time decision-making capabilities. Deploy best practices across your Big Data stack.
Prototype Development
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.
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.
Data Pipeline Optimization
Simplify and modernize your data pipeline: move away from batch processing and implement real-time streaming into your data lake.
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
Past Projects
Building a Big Data Pipeline With Cloud Native Tools
A statewide health care system
Airflow
Azure
Databricks
Docker
Python
Mapping Forest Fire Trajectory using GeoDjango
A data service provider that focuses on disaster planning, management, and response
Django
Python
AWS
Docker
FreeBSD/Linux
Latest Blog Posts
Improving Big Data: A Guide to Enhanced Pipelines
Learn how we streamlined data integration, minimized costs, and created a scalable process.
Too Big for DAG Factories?
As your infrastructure scales up, how you go about managing all DAGs in Airflow becomes very important.
Data Warehousing in Microsoft Azure
Data Warehousing is the process of storing and processing large amounts of data from many disparate sources.
How to Work with Us
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