16 December 2024

Early Christmas present,
courtesy of EFRAG

The CSRD expands ESG reporting across Europe. Learn key steps, data’s role, and deadlines to ensure compliance and accountability.

9 December 2024

Transforming business with machine learning: Insights from real-world applications

Explore four real-world examples of how machine learning is reshaping industries, improving customer experiences, streamlining operations, and driving innovation. Discover the transformative potential of ML for your business.

25 November 2024

Building a robust CSRD reporting strategy

Developing a strong CSRD strategy is key to staying competitive. Explore 7 steps to align with ESRS, achieve compliance, and foster growth.

6 November 2024

From AI Ideas to Impact: Roadmap for Successful Projects

Bringing AI ideas to life requires more than just ambition, it needs a clear roadmap. Discover how to go from concept to production with our step-by-step guide to successful AI implementation.

25 October 2024

Getting ahead of ESG reporting requirements

The CSRD expands ESG reporting across Europe. Learn key steps, data’s role, and deadlines to ensure compliance and accountability.

15 October 2024

Reliable Support Beyond Implementation with Our Romanian Team

Discover how fast, flexible support from Romania ensures smooth operations and cost-effective solutions for clients.

19 September 2024

Understanding data lakehouse table formats: A comparison of Apache Iceberg and Delta Lake

Apache Iceberg vs Delta Lake, a comparison

22 August 2024

Requirements Analysis no sexy buzzword, but nevertheless indispensable

14 August 2024

Embracing ESG (& CSRD)

In the wake of global challenges such as climate change, social inequality, and corporate governance failures, Environmental, Social, and Governance (ESG) principles have emerged as vital metrics for evaluating the sustainability and societal impact of businesses.

30 July 2024

Jump start
your data lake with our meta data driven blueprint

By leveraging metadata and cloud technology, we enabled faster data ingestion, improved data exploration, and delivered significant cost savings and business insights.

Unlocking the power of data orchestration
with Dagster

In the fast-paced world of data engineering, effective data orchestration is essential for managing complex workflows, ensuring data quality, and enabling efficient data cataloging. Dagster is a powerful framework that not only streamlines data management but also provides a robust asset-centric approach to help teams optimize their data operations. This blog explores what Dagster is and how it can transform your data platform.

AUTHOR – Joris

What is Dagster?

Dagster is a scalable framework designed to define, schedule, and monitor data pipelines in an asset-centric way. Unlike traditional task-based systems, it focuses on modeling data as assets, outputs of data computations, such as tables, allowing teams to track and manage data more effectively.

This asset-centric approach not only enhances data quality but also offers superior observability and a cockpit view of your data platform. By treating data as assets, Dagster simplifies complex data workflows and integrates easily with various data tools like dbt, Power BI, and Databricks, ensuring versatility in different environments.

Furthermore, Dagster helps enforce data quality rules, ensuring that only reliable data enters the pipeline, thereby reducing risks and operational costs.

Key benefits
and challenges
solved by Dagster

Dagster is designed to solve some of the most common challenges faced by data teams:

• Manages dependencies: Ensures smooth orchestration of data workflows across multiple systems.

• Tracks data lineage: Provides full visibility into data transformations and flow.

• Ensures data quality: Improves reliability and governance, critical for compliance.

• Simplifies data workflow orchestration: Helps teams manage complex data ecosystems with ease.

• Integrates seamlessly with multiple systems: Offers flexibility to work with a wide array of tools and technologies.

• Built-in observability: Empowers teams to monitor and optimize data performance effectively.

• Supports a wide range of applications: Ideal for managing customer data, training data science models, or overseeing business intelligence efforts.

• Improves efficiency and cost-effectiveness: Ensures that data operations remain streamlined, reliable, and cost-effective.

“Dagster simplifies complex data workflows, ensuring that only reliable data enters the pipeline, reducing operational risks and costs.”

Conclusion

Dagster stands out as a comprehensive solution for data orchestration, offering a range of features and benefits that cater to the needs of data teams and data-driven organisations.
By focusing on assets, enhancing observability, data quality, lineage and promoting collaboration, Dagster empowers teams to manage their data workflows with confidence.
As data operations continue to grow in complexity, Dagster provides the tools needed to navigate these challenges effectively and future-proof your data operations.

If you’re interested in exploring how Dagster can benefit your data operations, we encourage you to contact us, Acumen – an official Dagster implementation partner-, for more information.

This blog post is part of a blog post series. In the next two blog posts we compare Dagster with Airflow and show how we can help you to migrate easily and step-by-step from Airflow to Dagster.

Need our Dagster guidance?

Reach out to explore how we can support you with Dagster.

Want to explore Dagster?

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Early Christmas present, courtesy of EFRAG