Jumpstart your data processing with this local modern data stack template
Oct 25, 2024
Spark-based data PaaS solutions are convenient. But they come with their own set of challenges such as a high vendor lock-in and obscured costs. We show how to use a dedicated orchestrator ([dagster-pipes](https://docs.dagster.io/guides/dagster-pipes)). It can not only make Databricks an implementation detail but also save cost. Also, it improves developer productivity. It allows you to take back control.
Sep 12, 2024
Spark-based data PaaS solutions are convenient. But they come with their own set of challenges such as a high vendor lock-in and obscured costs. We show how to use a dedicated orchestrator ([dagster-pipes](https://docs.dagster.io/guides/dagster-pipes)). It can not only make Databricks an implementation detail but also save cost. Also, it improves developer productivity. It allows you to take back control.
Jun 21, 2024
Save money 💰 and increase developer productivity 👩💻👨💻 by limiting scope-creep of Spark-based data PaaS solutions: 🌐 turn them into an implementation detail 🔧.
Jun 21, 2024
Lean and efficient MDS experience: Delivers better software engineering practices to the data ecosystem with the new local MDS stack comprised of Dagster, dbt and DuckDB which offers better developer productivity by enhancing testability of the E2E pipeline.
Dec 11, 2023
📊 Unleash the power of metadata extraction in your data engineering pipelines with the new DBT API in Dagster! 🚀 Learn how to seamlessly integrate and leverage DBT transformations, while enriching your data catalog with advanced metadata. Elevate your data governance and collaboration to new heights!
Jun 13, 2023
The data orchestrator is at the heart of the data pipelines. We start by exploring how a modern data orchestrator drastically eases the development of pipelines. Then we will see how govanance can be conducted efficiently in a MDS-based setup.
Dec 8, 2022
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream \emph{high noise} in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.
May 10, 2022
The fragmented modern data stack has emerged as the unbundling of Airflow. Various tools operate in silos. Dagster as a next-generation data orchestrator allows you to clearly see the data dependencies of the individual pipelines on your data factory floor.
Apr 27, 2022
Towards simpler and perhaps more energy efficient data platforms with increased developer productivity.
Apr 2, 2022