Streamline your data pipelines to support real-time decision-making capabilities. Deploy best practices across your Big Data stack.

  • Prototype Development

    An iterative approach through quick proof-of-concepts will help validate your Big Data innovations faster than a waterfall approach.

  • 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

How to Work with Us

Flex 6 process diagram

Latest Blog Posts

Improving Big Data: A Guide to Enhanced Pipelines

Seeking to improve your big data pipeline? This blog walks you through enhancements made to a client's system using Airflow Datasets, DAG Dependencies, Azure Durable Functions, and edge cases. Learn how we added functionality and flexibility by streamlining data integration, minimizing cost increases, and creating a scalable pipeline development process.

Too Big for DAG Factories?

As your infrastructure scales up, how you go about managing all DAGs in Airflow becomes very important. One method would be to create a “DAG factory,” which can churn out thousands of DAGs dynamically from a single configuration file.

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