October 16, 202211 minute read

Dagster at all 5 steps of the development lifecycle


In data engineering, as in any software development discipline, it is essential to have a system in place that helps engineers build, test, and deploy their code efficiently and effectively. This is where the software development lifecycle comes in. The software development lifecycle is a process that helps developers organize and manage the various stages of software development, from writing code to testing, review, deployment, and monitoring.

Dagster is a data orchestration framework that is designed to help developers at every stage of the software development lifecycle. Our goal is to provide a single, unified platform that allows developers to write, test, review, deploy, and monitor their code, all in one place.

In the following sections, we will discuss the five stages of the software development lifecycle, explain why existing solutions like Airflow and Prefect have limitations in serving these stages, and make the case for why Dagster is the best solution for making data engineers and data engineering teams productive.

The five stages of the Software Development Lifecycle

The software development lifecycle is a process that helps developers organize and manage the various stages of software development.

While every organization has its own version of an SDLC, for the purposes of this blog post, we’ll focus on the following stages:

a logo of Dagster and a Google Sheet
  1. Writing code: This is the initial stage of the software development process after requirements are gathered, where developers write the code for their software. This stage involves creating the logic and functionality of the software, as well as writing any necessary documentation.
  2. Testing: After the code has been written, it is important to test it to ensure that it is working properly. This stage involves running the code through various tests to check for critical errors and minor bugs, and making any necessary changes to fix these issues.
  3. Review/collaboration: Once the code has been tested, it is important to review it to ensure that it meets the necessary standards and requirements. This stage involves collaboration between team members, who review the code and provide feedback to make any necessary changes.
  4. Deployment: After the code has been reviewed and any necessary changes have been made, it is ready to be deployed. This stage involves making the code available for use by end-users, and ensuring that it integrates properly into existing systems.
  5. Monitoring/observability: Once deployed, the final stage of the software development lifecycle is monitoring and observability. This stage involves keeping track of the performance of the software and making any necessary changes to ensure that its performance is optimized.

By following a structured process like the software development lifecycle, developers can avoid common pitfalls and ensure that their software is of the highest quality.

Let’s dig into each step of this process in detail.

1: Writing code

During this stage, developers create the logic and functionality of the software, as well as write any necessary documentation. This stage is critical in ensuring that the software is built correctly and functions as intended.

To write code effectively, developers need to have a deep understanding of the programming language they are using. It the realm of data engineering, the most commonly used language is Python because it is a high-level, general-purpose programming language that emphasizes code readability.

Developers must also have a strong handle on the requirements and constraints of the project. They must be able to think logically and be able to break down complex problems into smaller, manageable tasks.

Tools like Dagster can help developers in this stage by providing a unified platform for writing and organizing code. Dagster's declarative programming model allows developers to define the structure and behavior of their code in a clear and concise way, making it easier to write and understand.

Additionally, Dagster's support for software-defined assets allows developers to easily share and reuse code across different projects, saving time and effort. By providing these tools and features, Dagster helps developers write high-quality code efficiently and effectively.

Finally, Dagster provides the right tools for local development and continuous integration so that individual developers can be at their most productive while contributing to a larger collaborative project.

2: Testing

The second stage of the software development lifecycle is testing. After the code has been written, it is important to test it to ensure that it is working properly.

During this stage, developers run the code through various tests to check for errors and make any necessary changes to fix these issues. This stage is critical in ensuring that the software is reliable and performs as expected.

To test code effectively, developers need to have a thorough understanding of the functionality of the software, as well as the potential sources of errors and bugs. They must also be able to design and implement various test cases to ensure that the code is thoroughly tested.

Tools like Dagster can help developers in this stage by providing support for automated testing. Dagster's built-in testing framework allows developers to easily create and run test cases and view the results in a clear and intuitive way.

Additionally, Dagster's support for local development and testability allows developers to easily test individual components of the software in isolation, making it easier to find and fix any issues. By providing these tools and features, Dagster helps developers test their code efficiently and effectively.

3: Review / collaboration

The third stage of the software development lifecycle is review and collaboration. After the code has been tested, it is important to review it to ensure that it meets the necessary standards and requirements.

During this stage, team members review the code and provide feedback to make any necessary changes. This stage is critical in ensuring that the code is of high quality and meets the needs of the project.

To review code effectively, team members need to have a thorough understanding of the project requirements and constraints, as well as the functionality of the software. They must also be able to communicate effectively and provide constructive feedback to the developers.

Tools like Dagster can help teams in this stage by providing support for collaboration at the code review stage. Dagster Cloud's built-in support for branch deployments allows developers to easily create and test different versions of the code in an ephemeral staging environment, making it easier to incorporate feedback and make changes. By providing these tools and features, Dagster helps teams remain productive while reviewing and collaborating on code.

4: Deployment

The fourth stage of the software development lifecycle is deployment. After the code has been reviewed and any necessary changes have been made, it is ready to be deployed.

During this stage, the code is made available to end-users and is properly integrated into the existing system.

To deploy code effectively, developers need to have a thorough understanding of the existing system and how the new software will fit into it. They must also be able to manage the deployment process and ensure that the software is properly installed and configured.

Tools like Dagster can help developers in this stage by providing support for automated deployment. Dagster's built-in deployment tools allow developers to easily package and deploy their code and track the status of the deployment process.

Additionally, Dagster's support for config-as-code allows developers to easily manage and maintain the configuration of the software, making it easier to keep the system up-to-date and maintain consistency across different deployments.

5: Monitoring / observability

The fifth and final stage of the software development lifecycle is monitoring and observability. This stage involves keeping track of the performance of both the applications and the infrastructure they run on, and making any necessary changes to ensure that it is working properly.

During this stage, developers monitor the performance of the software, and track any issues or errors that may occur. They also make any necessary changes to the code or the system to improve the performance of the software.

To monitor and observe code effectively, developers need to have a thorough understanding of the functionality of the software, as well as the potential sources of performance issues. They must also be able to use various tools and techniques to track the performance of the software, and make any necessary changes.

Tools like Dagster can help developers in this stage by providing support for monitoring and observability. Dagster's built-in monitoring tools allow developers to easily identify parts of the data pipeline that are late or failing, and easily get notifications when the pipeline is likely to miss SLAs.

Furthermore, Dagster integrates with infrastructure monitoring platforms like Datadog, PagerDuty, or Splunk, making it easy to apply your existing investments in monitoring and observability to the data domain.

Existing Solutions and Their Limitations

While there are many tools and frameworks available to help developers at various stages of the software development lifecycle, many of these solutions have limitations that make them less effective than Dagster.

For example, existing solutions like Airflow and Prefect are popular choices for managing data pipelines, but they have limitations when it comes to serving other stages of the software development lifecycle.

Airflow, for instance, is a powerful tool for managing data pipelines, but it lacks support for many of the other stages of the software development lifecycle. It does not provide built-in tools for testing, review and collaboration, deployment, or monitoring and observability, making it difficult for developers to use Airflow for these stages of the process.

Prefect is another popular tool for managing data pipelines, but it also has limitations when it comes to serving the entire software development lifecycle. Prefect provides some support for testing, but it does not have built-in tools for review and collaboration or monitoring and observability.

These limitations are problematic for developers, who need a single, unified platform that can help them at every stage of the software development lifecycle. With Dagster, these considerations have been built in from the ground up.

In the following section, we will explain how Dagster's existing technological investments have already served data engineers in several phases of the software development lifecycle and how we plan on rolling out additional features to serve developers at every stage.

Dagster’s Strategy for Serving the Entire Development Lifecycle

Dagster is a data orchestration framework that is designed to serve developers at every stage of the software development lifecycle. Our existing technological investments have already helped users in several phases of the software development process, and we plan on rolling out additional features to serve developers at every stage.

Branch deployments

One of the key features of Dagster is its support for branch deployments. This allows developers to easily create and test different versions of the code, making it easier to incorporate feedback and make changes. This feature has already served engineers in the review and collaboration stage of the software development lifecycle, and we plan to improve it continuously in the future.

Software-defined assets

Another key feature of Dagster is its support for declarative programming via software-defined assets. With SDAs, developers can define the structure and behavior of their code in a clear and concise way, making it easier to write and understand. Furthermore, the use of SDAs as a core abstraction in the Dagster framework opens up many more features related to observability, scheduling, and reusability. Software-defined assets have already served Dagster users (both core and Cloud) in the code writing stage of the software development lifecycle, and we plan on continuing to improve ion this abstraction in the future.

Built-in testability

Finally, Dagster allows developers to easily test individual components of the software in isolation, making it easier to find and fix any issues. Dagster’s Resources system abstracts away all interaction with outside systems, making it easy to swap in test doubles, mocks, or other alternative implementations while under test. Dagster’s software-defined assets also enable this, as IO is separated from business logic using the IOManager abstraction. This enables practitioners to test the business logic without spinning up an expensive and slow data lake or cloud data warehouse to run the test.

In conclusion, Dagster's existing technological investments have already helped users in several phases of the software development lifecycle, and we plan on continuing to improve and expand our features to serve developers at every stage of the process.

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Exciting new developments

We are excited to announce several new developments in Dagster that will help data practitioners at every stage of the software development lifecycle. Here are some deep dives into our new config-as-code and declarative scheduling features, which will help improve the efficiency and performance of data pipelines.

Config-as-code

Config-as-code is a software development practice that involves storing configuration data in files that can be managed and versioned like any other code. This allows developers to easily manage and maintain the configuration of their software, making it easier to keep the system up-to-date and maintain consistency across different deployments.

One of the key benefits of config-as-code is that it allows developers to increase their velocity and quality. By storing configuration data in files that can be managed and versioned like any other code, developers can easily make changes and updates to the configuration and quickly roll back to previous versions if necessary.

Additionally, config-as-code allows developers to automate the configuration process, reducing the need for manual intervention and eliminating potential errors. This can help improve the reliability and performance of the software and reduce the time and effort required to manage the configuration.

In summary, config-as-code is a valuable practice that can help developers increase their velocity and quality, and Dagster will continue to evolve the concept of config-as-code to serve developers at every stage of the software development lifecycle.

Declarative scheduling

Declarative scheduling is a key feature of Dagster that allows developers to apply "freshness policies" to their software-defined assets. These policies specify the conditions under which an asset should be refreshed, and Dagster can use these policies to automatically determine when an asset needs to be refreshed.

This approach is in contrast to task-oriented approaches like Airflow and Prefect, where tasks must run on a schedule or be explicitly triggered. In these task-centric systems, it can be difficult to reason about the freshness of assets when they are shared between multiple jobs, and it is easy to end up refreshing assets unnecessarily, which can be costly.

Declarative scheduling in Dagster solves this problem by allowing developers to specify freshness policies for their assets. This allows Dagster to automatically determine when an asset needs to be refreshed based on the conditions specified in the policy. This can help save money and resources and improve the overall efficiency of the data pipeline.

In summary, declarative scheduling is a valuable feature of Dagster that allows developers to apply freshness policies to their software-defined assets. This can help save money and resources and improve the efficiency of the data pipeline. It is a superior approach to task-oriented systems like Airflow and Prefect, which make it harder to pin down the exact status of critical data assets leading to unnecessary refreshes and rebuilds.

Dagster: designed to serve every stage of the development lifecycle

In conclusion, Dagster is a data orchestration framework that is designed to serve developers at every stage of the software development lifecycle. Our existing technological investments have already helped users in several phases of the software development process, and we plan on rolling out additional features to serve developers at every stage.

Dagster's support for branch deployments, local development, declarative programming via software-defined assets, and testability already serve data engineers in several phases of the software development lifecycle. Additionally, our plans to roll out support for config-as-code and declarative scheduling will help developers at every stage of the software development process.

You can try Dagster today with our free Serverless Cloud trial. If you would like to support the open-source project, we encourage you to Star the GitHub repo.

We're always happy to hear your feedback, so please reach out to us! If you have any questions, ask them in the Dagster community Slack (join here!) or start a Github discussion. If you run into any bugs, let us know with a Github issue. And if you're interested in working with us, check out our open roles!

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