Artificial Intelligence Engineer at a consultancy with 11-50 employees
Real User
Top 20
Jun 25, 2026
The main use case for using Dagster Labs is to utilize ETL processes such as extract, transform, and load because we already have raw data, and we perform all the necessary transformations, including feature engineering and mathematical calculations, and then we load it to the front end so that customers can see how their data has evolved into something useful, allowing them to make actionable decisions. Let me consider a scenario where a customer has raw and unreadable data. We need to identify features first. For example, a customer may have IP addresses with last seen timestamps along with raw security logs. During the initial feature engineering phase, we check for use cases such as detecting if a user on the customer side is using unauthorized resources. To accomplish this, we need to perform feature engineering on several attributes, such as the user's authorization level and the objects they are accessing. For a second example, we can detect if a user is authorizing company access after work hours by using the last seen attribute. We can perform feature engineering on such scenarios. That covers my main use case, but we are also currently trying to use the DLT tool by using Dagster Labs so that we can extract the data as well.
Senior Solutions Analyst at Thoughtspot Private Limited
Real User
Top 10
Jun 22, 2026
I primarily use Dagster Labs for asset-based orchestration, scheduling and automating pipelines, and visualizing dependencies between datasets, and testing it. I test unit test cases in the pipeline. A practical example of how I use Dagster Labs for asset-based orchestration is when I take the raw orders, clean orders, sales fact tables, daily revenue summary, and VBA dashboard. I use Dagster Labs to track the dependency between these assets. When a raw table arrives, the dependent assets are recomputed. If the sales table fails due to a schema change, Dagster Labs prevents downstream assets from running, highlighting the failed assets in the lineage graph. This allows me to check the logic behind the data, and it also allows me to rematerialize only the affected assets after the fix. This asset-centric approach makes it more reliable and easier to debug when orchestrating individual assets. I also use Dagster Labs to create scheduled pipeline jobs for running the pipeline.
Analytics Engineer at a retailer with 1,001-5,000 employees
Real User
Top 20
Jun 19, 2026
I perform orchestration around data by connecting raw app data using Fivetran inside BigQuery warehouse to DBT. I do this entire process using Dagster Labs. My workflow is that Fivetran reads data from the raw data source and sends the data to BigQuery. From BigQuery, the data is read inside the BI dashboards. The entire pipeline from raw to Fivetran to BigQuery to BI dashboard is monitored inside Dagster Labs.
Dagster Labs empowers data-centric enterprises to build, manage, and orchestrate complex data pipelines with ease. Its flexible architecture supports efficient data processing, enhancing productivity.
Dagster Labs provides a robust platform designed for performance and scalability, catering to developers and data engineers seeking to streamline data workflow management. It integrates seamlessly with modern data tools, facilitating efficient data pipeline execution and monitoring. Users...
The main use case for using Dagster Labs is to utilize ETL processes such as extract, transform, and load because we already have raw data, and we perform all the necessary transformations, including feature engineering and mathematical calculations, and then we load it to the front end so that customers can see how their data has evolved into something useful, allowing them to make actionable decisions. Let me consider a scenario where a customer has raw and unreadable data. We need to identify features first. For example, a customer may have IP addresses with last seen timestamps along with raw security logs. During the initial feature engineering phase, we check for use cases such as detecting if a user on the customer side is using unauthorized resources. To accomplish this, we need to perform feature engineering on several attributes, such as the user's authorization level and the objects they are accessing. For a second example, we can detect if a user is authorizing company access after work hours by using the last seen attribute. We can perform feature engineering on such scenarios. That covers my main use case, but we are also currently trying to use the DLT tool by using Dagster Labs so that we can extract the data as well.
I primarily use Dagster Labs for asset-based orchestration, scheduling and automating pipelines, and visualizing dependencies between datasets, and testing it. I test unit test cases in the pipeline. A practical example of how I use Dagster Labs for asset-based orchestration is when I take the raw orders, clean orders, sales fact tables, daily revenue summary, and VBA dashboard. I use Dagster Labs to track the dependency between these assets. When a raw table arrives, the dependent assets are recomputed. If the sales table fails due to a schema change, Dagster Labs prevents downstream assets from running, highlighting the failed assets in the lineage graph. This allows me to check the logic behind the data, and it also allows me to rematerialize only the affected assets after the fix. This asset-centric approach makes it more reliable and easier to debug when orchestrating individual assets. I also use Dagster Labs to create scheduled pipeline jobs for running the pipeline.
I perform orchestration around data by connecting raw app data using Fivetran inside BigQuery warehouse to DBT. I do this entire process using Dagster Labs. My workflow is that Fivetran reads data from the raw data source and sends the data to BigQuery. From BigQuery, the data is read inside the BI dashboards. The entire pipeline from raw to Fivetran to BigQuery to BI dashboard is monitored inside Dagster Labs.