[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-dagster":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"The asset-oriented data orchestrator for declarative, testable and observable pipelines.","Dagster is an open-source orchestrator that thinks in assets rather than tasks: every table, file or model is declared in Python as a software-defined asset, and the dependency graph, lineage and freshness follow from it. Partitions and backfills replay a date range, asset checks validate quality on every materialization, and dbt, Spark, Snowflake or Airbyte integrations plug into the same graph. It's our choice when a data team wants to test pipelines locally and know, asset by asset, what is up to date.",[7,8,9,10],"Software-defined assets: lineage, dependencies and freshness derived from code.","Partitions and backfills: replay a date range in one click.","Asset checks and declarative automation: rule-driven quality and refreshes.","Testable local development, native dbt integration, managed Dagster+ with branch deployments.","ELT and dbt orchestration, ML feature pipelines, internal data platforms, migration from Airflow.",1792592354056]