Data Stack

In depth

A modern data stack typically has three main layers: data ingestion, data storage, and data consumption.

The data ingestion layer includes the tools that move data from sources—such as apps, databases, or spreadsheets—into your stack. You might use a connector service or an API to automate this flow.

The data storage layer is where your data lives once it’s collected. This often means a cloud data warehouse or data lake designed for fast queries and scalable capacity. This is where you apply transformations and governance to keep data clean and compliant.

The data consumption layer is the front end of the stack. Dashboards, reports and analytics platforms sit here to help teams explore metrics, build visualizations and share insights. This layer turns raw data into actionable information.

Over time, stacks have evolved to include semantic layers, metric catalogs and AI capabilities. These additions add a governance and user-friendly layer, ensuring everyone in your organization uses the same trusted definitions and can self-serve analytics without bottlenecks.

Pro tip

Build your stack with modular components. That way, you can swap or upgrade a single layer—whether it’s a new warehouse or an advanced analytics tool—without rebuilding everything from scratch.

Why the Data Stack matters

• Consistency and trust: A well-organized, efficient data stack prevents information silos and conflicting reports.

• Scalability: As data volumes grow, a well-architected stack scales with minimal overhead.

• Speed to insight: Automated pipelines and self-serve analytics mean business users get fast access to essential data.

Data Stack - In practice

Imagine an e-commerce retailer that’s transitioned from manual exports and static spreadsheets to an organized, automated, near-real-time data stack:

1. A connector service pulls sales and marketing data into their cloud warehouse on an hourly schedule.
2. In the warehouse, they run transformations to clean customer and product tables.
3. A data analysis tool, like PowerMetrics, connects directly to data in the warehouse. Teams build self-serve dashboards on sales, website traffic, and profit margin.
4. Department heads set up goals and notifications so they can track metric progress.

Your Data Stack and PowerMetrics

In Klipfolio PowerMetrics, your data stack extends from spreadsheets, APIs, and data warehouses all the way through to self-serve dashboards. Depending on your needs, PowerMetrics can play multiple roles, including handling data ingestion and storage and performing data modelling. PowerMetrics includes a built-in metrics layer and a centralized metrics catalog to ensure data quality and consistency and enable interactive exploration and presentation.

With PMQL, PowerMetrics also allows advanced querying and data-out capabilities, empowering business users to slice, filter, and analyze metrics confidently—all within a single, unified platform.