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The Modern Data Stack: Empowering Data-Driven Organizations with Dagster, dbt, Great Expectations, Superset, and DataHub

Posted on:February 29, 2024

In today’s data-driven landscape, organizations are constantly seeking ways to efficiently manage, transform, and derive insights from their ever-growing data assets. The modern data stack has emerged as a powerful solution, combining best-in-class tools to streamline data workflows and enable data-driven decision-making. In this blog post, we’ll explore five key components of the modern data stack: Dagster, dbt, Great Expectations, Superset, and DataHub.

Dagster: Orchestrating Data Assets

Dagster is a data orchestrator designed for developing and maintaining data assets, such as tables, datasets, machine learning models, and reports. With Dagster, you can declare functions that produce or update data assets and specify when and how they should run. Dagster helps you keep your assets up-to-date by orchestrating the execution of your functions at the right time.

One of the key advantages of Dagster is its versatility. It can be used at every stage of the data development lifecycle, from local development and testing to staging environments and production deployments. Dagster provides a unified framework for managing data workflows, making it easier to collaborate, test, and deploy data pipelines.

dbt: Transforming Data with Confidence

dbt (Data Build Tool) is a transformation workflow that empowers data teams to work more efficiently while delivering higher-quality results. With dbt, you can modularize and centralize your analytics code, applying software engineering best practices to your data transformations.

dbt allows you to collaborate on data models, version control your transformations, and test and document your queries before safely deploying them to production. It provides guardrails and visibility, ensuring that your data transformations are reliable and maintainable.

Great Expectations: Validating and Profiling Data

Great Expectations is a leading tool for validating, documenting, and profiling your data to maintain quality and improve communication between teams. Just as automated testing is essential for managing complex codebases, Great Expectations brings the same discipline and confidence to data science and data engineering workflows.

With Great Expectations, you can define data quality expectations, validate your data against those expectations, and generate comprehensive data documentation. This helps ensure that your data meets the required standards and facilitates effective collaboration between data producers and consumers.

Superset: Visualizing Data with Ease

Apache Superset is a modern, enterprise-ready business intelligence web application. It provides a fast, lightweight, and intuitive interface for exploring and visualizing data. Superset supports a wide range of chart types, from simple pie charts to complex geospatial visualizations.

Superset’s user-friendly interface makes it accessible to users of all skill levels, enabling them to create insightful dashboards and perform ad-hoc data exploration. Its integration with various data sources and compatibility with the modern data stack make it a powerful tool for data visualization and reporting.

DataHub: Cataloging and Governing Data

DataHub is a modern data catalog designed to enable end-to-end data discovery, observability, and governance. As an extensible metadata platform, DataHub helps organizations tame the complexity of their rapidly evolving data ecosystems and maximize the value of their data assets.

With DataHub, data practitioners can easily discover and understand their data, track data lineage, and enforce data governance policies. It provides a central repository for metadata, making it easier to collaborate, share knowledge, and ensure data quality and compliance.


The modern data stack, comprising tools like Dagster, dbt, Great Expectations, Superset, and DataHub, empowers organizations to efficiently manage, transform, validate, visualize, and govern their data. By leveraging these best-in-class tools, data teams can streamline their workflows, improve data quality, and derive valuable insights from their data assets.

Adopting the modern data stack enables organizations to capitalize on the full potential of their data, driving innovation and informed decision-making. As data continues to grow in volume and complexity, the modern data stack provides a robust and scalable framework for navigating the data landscape and achieving data-driven success.