How We Build Scalable Data Platforms

We design and implement modular data platforms that are flexible, efficient, and built to scale.

Our process transforms complex data development workflows & operations into reliable, automated systems.

Learn more about how we work

Our Modular Data Platform Framework

Our approach prioritizes modularity. Rather than focusing solely on individual tools, we design platforms where components integrate seamlessly. A streamlined developer experience ensures adaptability, operational efficiency, and long-term scalability.

What Is a Modular Data Platform?

A modular data platform organizes data processing into distinct, interconnected components designed for a specific function. Each tool is optimized for a specific function and integrated through clear interfaces. Such architecture enhances scalability, simplifies maintenance, and improves reliability. It also enables automation through CI/CD pipelines while isolating failures to specific modules.

Learn More About Modular Data Platforms
Data platform architecture schema with tools and technologies

Our Process

We follow a structured, two-stage process to deliver scalable, automated data solutions.

Stage 1 Blueprint

Architecture Review

What we do?

We assess your infrastructure and compare it to best-practice reference architectures.

Outcomes:

  • Identification of architecture gaps (missing capabilities or inefficiencies).
  • An inventory of tools, processes, and integrations.
  • An outline of how users create and manage analytical models.
  • Evaluation of the platform’s scalability and automation readiness.

Why Our Process Works

Our process is built on experience and real-world implementation. These principles guide every project:

  1. 1

    Modular Design

    Ensures flexibility by focusing on well-defined components and interfaces.

  2. 2

    Automation First

    Reduces manual effort and improves reliability.

  3. 3

    Outcome Based

    We focus on clarifying deliverables and scope at each step of the process, so you pay for results rather than just billable hours.

FAQ

Curious to know more? Schedule a call and get all your questions answered!

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What is the typical timeline of the collaboration?

The timeline depends on the project scope, existing infrastructure, and specific business needs. Typically:

  • The blueprint phase takes 8-12 weeks, where we assess your architecture and define a roadmap.
  • The release phase varies based on implementation complexity, but our goal is for each release to take between 4 to 8 weeks.
How much involvement is required from our team?
  • Blueprint phase: Approximately 24 hours in total, mainly for workshops, architecture reviews, and roadmap validation.
  • Releases phase: Around 6 to 12 hours, depending on the complexity and level of internal collaboration needed for deployment and integration.
When can we expect to see the first outcomes?

The first MVP is typically delivered during the blueprint phase, usually 8-12 weeks after the collaboration kick-off. This allows for early validation of key assumptions and quick value realization.

Can we engage with The Scalable Way for just the blueprint phase?

Yes. You can choose to work with us only on the Blueprint phase, where we focus on assessing your current setup, identifying gaps, and creating a detailed roadmap. This provides a clear implementation strategy, whether you proceed with us or handle execution internally.

Can you integrate with our existing technology stack?

Yes, but while our modular approach ensures seamless integration with your existing tools and infrastructure, our focus on automation and good developer experience means we would not be a good fit if your goal is to use mostly UI-based ETL tools.

Who from my team should be involved in the project?

Key participants should include:

  • Data engineers to provide technical insights and collaborate on architecture design and implementation.
  • Interested stakeholders, such as product owners, analysts, or business leaders, should align the solution with organizational goals and ensure business value.

Our Insights

Selected Articles. Check our blog for more.

CI/CD for Data Workflows: Automating Prefect Deployments with GitHub Actions

prefect prefect worker github actions CI/CD data workflows data platform architecture

The final part of the Data Platform Infrastructure on GCP series covers CI/CD for Prefect deployments using GitHub Actions and Docker. Automate flow builds, worker updates, and streamline orchestration across environments.

Scaling Secure Data Access: A Systematic RBAC Approach Using Entra ID

Data Governance Access Management RBAC Entra ID Security Architecture

Establish scalable, secure access controls for your data platform with a systematic RBAC strategy built on Microsoft Entra ID. This article outlines a five-phase implementation—from user persona mapping to automated auditing—designed to balance flexibility, compliance, and operational efficiency.

Getting to Your First Flow Run: Prefect Worker & Deployment Setup

prefect data platform architecture workflow orchestration Kubernetes

Run your first data ingestion workflow with Prefect, Docker, and Kubernetes. This guide walks through containerized flow execution, Prefect worker deployment, and clean deployment configs, laying the foundation for a scalable, maintainable orchestration layer.

Roles in the Context of the Analytics Workflow

governance analytics workflow roles

Code-based analytics workflows offer a unique advantage - the ability to combine robust collaboration with strict governance. While technically straightforward to implement, this approach only reaches its full potential when aligned with well-adapted business and analytics processes.