About Dagster Labs

Dagster Labs provides a cloud-native orchestration platform that enables data engineers to manage complex data pipelines using software-defined assets and a declarative programming model. This solution enhances data pipeline velocity and reliability through integrated lineage, observability, and first-class testing, addressing the challenges of data complexity and operational inefficiencies.

```xml <problem> Data engineers face challenges in managing complex data pipelines, leading to inefficiencies and reliability issues. Traditional orchestration tools often lack integrated lineage, observability, and testing capabilities, hindering data pipeline velocity and increasing operational overhead. </problem> <solution> Dagster Labs offers a cloud-native orchestration platform designed to streamline the management of intricate data pipelines. By employing software-defined assets and a declarative programming model, Dagster enhances data pipeline velocity and reliability. The platform provides integrated lineage tracking, observability features, and first-class testing capabilities, enabling data engineers to effectively manage data complexity. Dagster's asset-oriented approach allows for comprehensive monitoring, debugging, and inspection of data assets within a single pane of glass. </solution> <features> - Software-defined assets for managing data dependencies and execution flow - Declarative programming model for defining data pipelines as code - Integrated lineage tracking to visualize data dependencies and transformations - Observability features for monitoring pipeline execution and identifying bottlenecks - First-class testing capabilities for ensuring data quality and pipeline reliability - Cloud-native architecture for scalable and resilient deployments - Support for Python assets and dbt-native orchestration - Integration with Airbyte and other tools in the modern data stack </features> <target_audience> The primary target audience includes data engineers and data platform teams seeking to improve the velocity, reliability, and manageability of their data pipelines. </target_audience> <revenue_model> Dagster offers a commercial version called Dagster+ with a 30-day free trial, suggesting a subscription-based revenue model for enterprise features and support. </revenue_model> ```

What does Dagster Labs do?

Where is Dagster Labs located?

Dagster Labs is based in San Francisco, United States.

When was Dagster Labs founded?

Dagster Labs was founded in 2018.

How much funding has Dagster Labs raised?

Dagster Labs has raised 49220000.

Location

San Francisco, United States

Founded

2018

Funding

49220000

Employees

86 employees

Major Investors

Georgian

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Dagster Labs

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Executive Summary

dagster.io 10K+

Crunchbase

Founded 2018 – San Francisco, United States

Funding

Estimated Funding

$20M+

Major Investors

Georgian

Team (75+)

No team information available.

Company Description

Problem

Data engineers face challenges in managing complex data pipelines, leading to inefficiencies and reliability issues. Traditional orchestration tools often lack integrated lineage, observability, and testing capabilities, hindering data pipeline velocity and increasing operational overhead.

Solution

Dagster Labs offers a cloud-native orchestration platform designed to streamline the management of intricate data pipelines. By employing software-defined assets and a declarative programming model, Dagster enhances data pipeline velocity and reliability. The platform provides integrated lineage tracking, observability features, and first-class testing capabilities, enabling data engineers to effectively manage data complexity. Dagster's asset-oriented approach allows for comprehensive monitoring, debugging, and inspection of data assets within a single pane of glass.

Features

Software-defined assets for managing data dependencies and execution flow

Declarative programming model for defining data pipelines as code

Integrated lineage tracking to visualize data dependencies and transformations

Observability features for monitoring pipeline execution and identifying bottlenecks

First-class testing capabilities for ensuring data quality and pipeline reliability

Cloud-native architecture for scalable and resilient deployments

Support for Python assets and dbt-native orchestration

Integration with Airbyte and other tools in the modern data stack

Target Audience

The primary target audience includes data engineers and data platform teams seeking to improve the velocity, reliability, and manageability of their data pipelines.

Revenue Model

Dagster offers a commercial version called Dagster+ with a 30-day free trial, suggesting a subscription-based revenue model for enterprise features and support.