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

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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.

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.

How to Setup Data Platform Infrastructure on Google Cloud Platform with Terraform

google cloud platform terraform data platform architecture prefect

Learn how to set up a secure, scalable data platform infrastructure on Google Cloud Platform (GCP) using Terraform. This step-by-step guide covers VPC configuration, Compute Engine setup, firewall rules, Identity-Aware Proxy (IAP), Cloud NAT, and more, ensuring a cost-effective, flexible, and secure foundation for your data platform.

Organizing Networking for Data Platforms: Key Connectivity Options

networking data platform architecture elt process connectivity

Optimize your data platform by making informed networking decisions. This article explores how networking impacts ELT workflows, covering key connectivity options, security considerations, and best practices. Learn how to design a secure, scalable, and high-performing data platform architecture with the right networking.

Breaking Down Prefect Deployments To Improve The Data Ops Efficiency

prefect dataops elt pipelines

Discover how breaking down monolithic ETL flows into modular deployments enhances observability, streamlines troubleshooting, and boosts scalability. Learn to design data pipelines that evolve with your needs while maintaining performance and reliability.