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Transitioning to an asset-centric approach like Dagster's can be a game-changer for data teams. It can streamline workflows, improve efficiency, and provide a more holistic view of data assets. But how can teams make this transition smoothly and effectively? Let's explore.
There are a few approaches teams can take when transitioning towards an asset-centric approach like Dagster. For teams currently using dbt Core, they can plug dbt directly into Dagster and get the full benefits of Dagster while still leveraging dbt. Take a problem-driven approach - if your current approach is working fine, you may not need to transition immediately. But as complexity increases and you need things like better monitoring, orchestrating third-party processes, or more flexible scheduling, then transitioning to an asset-centric orchestrator like Dagster can help.
For startups, the transition can be more gradual as complexity increases over time. You can start with a cron scheduler, and move to Dagster when you need more capabilities like scheduling assets to update every 15 minutes.
For enterprises, it may be about simplifying existing complex systems. If you have 15 different orchestration systems today, moving to just 3-4 including Dagster can help consolidate and provide an asset-centric view.
Using Dagster's features like code locations and branch deployments can enable a decentralized but coordinated model, where each team has isolated spaces but can still rely on shared assets/data. This model promotes autonomy while ensuring consistency and reliability across the organization.
The key to a successful transition is taking an incremental, problem-driven approach, getting stakeholder buy-in, and not trying to overhaul everything at once. Start with the areas causing the most pain or complexity first. This approach reduces risk and allows for adjustments and learning along the way.
Some other ways to smooth the transition to an asset-centric approach like Dagster's include:
Dagster centralizes the creation of reusable data models and pipelines, while allowing decentralized teams to apply these assets to their own contexts. This promotes efficiency and consistency across projects.
Dagster is designed for use at every stage of the data development lifecycle, including local development, unit testing, CI, code review, staging, and debugging. This flexibility makes it easier for teams to adopt and integrate Dagster into their workflows.
Each team member has their own dev environment within which they work. They can clone tables from production into their dev datasets, saving time and computing costs.
If an error occurs upstream, the team notifies the stakeholders of the impact on the dashboards. This proactive communication helps maintain trust and transparency.
Conducting a limited POC can be a great way to test Dagster and build confidence that it's the better solution, then lifting and shifting pipelines to Dagster. This process can take a few days to a couple of weeks.
Secoda is an AI-powered solution that creates a single source of truth for an organization's data by connecting to all data sources, models, pipelines, databases, warehouses, and visualization tools. Regardless of technical ability, it is the easiest way for any data or business stakeholder to turn their insights into action.
Integrating Secoda into a Dagster workflow can bring several benefits. It can help teams monitor and simplify their data stack, collaborate and share data assets more efficiently, automate documentation, write dbt code, and let business users answer their own questions. It can also help discover effective strategies to reduce expenses and optimize maintenance for legacy data systems and infrastructure.