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Modern data stack

Modern data stack is a comprehensive framework and set of tools designed to efficiently collect, process, analyze, and visualize data in a well-integrated cloud-based data platform. The main characteristics of a modern data stack include robustness, speed, and scalability. Compared to traditional fragmented architectures, the core assumption of the modern data stack is unified access to data across the business.

A typical modern data stack consists of the following:

  • Cloud data warehouse
  • Extract, Load, Transform (ELT) tools
  • Data ingestion/integration services
  • Reverse Extract, Transform, Load (ETL) tools
  • Data orchestration tools
  • Business intelligence (BI) platforms

The entire premise behind a modern data stack is to create an end-to-end flow of data from acquisition to integration, to persistence – where every layer is centered around the analytics platform.

Further reading:

  • Privacy by design in practice: How “just enough” data beats “just in case” collection

    While collecting more data “just in case” feels safer, according to Matt Gershoff, it’s also one of the biggest sources of unnecessary compliance risk, analytical noise, and wasted organizational resources in the analytics industry today. His approach of “just enough” data collection is more intentional, more aligned with privacy regulation, and often more analytically effective.

  • 4 ways to make your analytics HIPAA-compliant: Implementation guide

    Healthcare organizations have four main approaches to achieving HIPAA-compliant analytics. Each has different trade-offs in cost, technical complexity, and analytics capabilities. This guide compares all four implementation methods – from using Google Analytics with workarounds to deploying fully HIPAA-compliant analytics platforms – so you can choose the right approach for your organization’s needs and resources.

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