The Modern Data Stack

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What is the Modern data stack?

A radically new approach to data integration saves engineering time, allowing engineers and analysts to pursue higher-value activities.

The modern data stack (MDS) is a suite of tools used for data integration. These tools include, in order of how the data flows:

a fully managed ELT data pipeline

a cloud-based columnar warehouse or data lake as a destination

a data transformation tool a business intelligence or data visualization platform.

The goal of an MDS is to analyze your business’s data to proactively uncover new areas of opportunity and improve efficiency. Here are some answers to common questions from our friends at Fivetran.

  • What should I look for in each component of the modern data stack?

    The modern data stack encompasses a data pipeline, a destination, a transformation layer and a business intelligence/data visualization platform.

  • How hard is it to set up?

    It’s easy! As it is hosted in the cloud and abstracts away complications from configuring infrastructure, modern data stacks today can easily be set up in less than an hour.

  • What are the benefits of a modern data stack?

    The modern data stack saves time, money and effort. The low and declining costs of cloud computing and storage continue to increase the cost savings of a modern data stack compared with on-premise solutions. Off-the-shelf connectors save considerable engineering time otherwise spent designing, building and maintaining data connectors, leaving your analysts, data scientists and data engineers free to pursue higher-value analytics and data science projects.

  • What separates a modern data stack from a legacy data stack?

    The most important difference between a modern data stack and a legacy data stack is that the modern data stack is hosted in the cloud and requires little technical configuration by the user. These characteristics promote end-user accessibility as well as scalability to quickly meet your growing data needs without the costly, lengthy downtime associated with scaling local server instances. The components of the modern data stack are built with analysts and business users in mind, meaning that users of all backgrounds can not only easily use these tools, but also administer them without in-depth technical knowledge.