Dagster resources for data engineers

Software-defined assets introduce declarative data engineering to manage tables, models, and dashboards as code. Dagster infers asset graphs from function dependencies rather than manual DAGs, shifting orchestration from isolated tasks directly to data products.

Dagster Labs brought Retain.ai into its fold to expand Dagster's data orchestration capabilities. Key Retain.ai personnel, including cofounder Eric Chernoff, join to accelerate development on developer-centric features and support Dagster Cloud's aggressive product roadmap.

Sessions focus on Kimball-style dimensional modeling, where DIMO translates business context and live warehouse metadata into runnable dbt projects with tests. Other talks cover governing data context for AI agents and running a lightweight data stack with dlt, DuckDB, dbt, Dagster, and Elementary.

Coding agents build and repair pipelines end to end using MCP when assets, quality checks, and lineage are stored as code files. Agents generate ingestion, SQL models, and tests from natural language prompts, then validate the dependency graph and lineage before execution.

Kestra 2.0 orchestrates workflows using declarative YAML over existing code. Its new Controller decouples workers from the database, letting them run in isolated networks via outbound gRPC without inbound ports. This architecture avoids direct database credentials on workers.

Dagster+ Hybrid isolates control from execution: Dagster hosts the control plane, while users run agents, code servers, and pipelines on AKS. Platform teams manage cluster infrastructure, ArgoCD GitOps, and Azure identities, letting data teams ship pipeline code through simple PRs.

Prefect and Dagster represent two distinct paradigms. Prefect orchestrates what code does via procedural tasks and flows, whereas Dagster orchestrates what data is. By declaring desired data assets and freshness, Dagster determines execution steps backward from the target outcome.

Small companies do not need Big Data to benefit from a data platform. Centralizing sources using open tools like Dagster, dltHub, dbt, and self-hosted ClickHouse on a $20 VPS reveals critical operational cross-source insights.

Modern data stacks are shifting toward declarative, code-first workflows using tools like Kubernetes, dbt, SQLMesh, and YAML configs. A declarative data stack is a set of tools and, precisely, its configs can be thought of as a single function. This provides reproducible and idempotent execution.

The Open Data Stack relies on open source and open standards across the data engineering lifecycle. Core tools like Airbyte handle ingestion with pre-built connectors, while dbt manages SQL transformations.

A declarative data stack automates modern infrastructure by separating business logic from technical implementation using configuration files. Engineers can build data stack compilers that generate production-ready pipelines, enforce data governance, and allow local testing before deployment.

Declarative data stacks use YAML configurations to automate ingestion, SQL transformations, and orchestration via tools like Airflow or Dagster. Starlake serves as an open-source warehouse orchestrator to reduce code complexity while maintaining governance.

Data orchestration is shifting from imperative pipelines to declarative, data-aware asset management. Modern tools like Airflow, Prefect, and Dagster treat data products as first-class objects. A Data Orchestrator models dependencies between different tasks in heterogeneous environments end-to-end.

Dagster allows defining Airbyte integration as code using Python to dynamically handle changing configs. "Dagster offers the interfaces that we can define our Airbyte connections with Python" to automatically apply syncs based on dynamic sources like scraping or evolving APIs.

Build a real-estate pipeline running on Kubernetes. Scrape property data, store it in MinIO S3, and process UPSERTs using Delta Lake. "Starting with web scraping gives you the power to treat every website as a database." Ingest data into Apache Druid and visualize it using Apache Superset.

GraphQL provides a unified data layer by acting as a single API endpoint for multiple backend systems. "GraphQL is SQL on steroids for APIs." It decouples API schemas from underlying database schemas, providing built-in type validation and automatic self-documentation for data teams.

Dagster Declarative Automation replaces imperative schedules with state-driven orchestration. Engineers declare an asset's up-to-date state while Dagster's daemon continuously evaluates graph dependencies and partitions to run required work. "Define the outcome, not the orchestration."

Data engineers build creative applications like SQLBuild using Dagster. SQLBuild is a stateless SQL transformation framework featuring asset-first lineage and integrated testing. Its developer claims, having lineage as a first-class concept just felt like the right abstraction for data engineering.

Designing pipelines for both incremental loads and historical backfills prevents fragile migration scripts. Using Dagster and DuckDB, the same transform loop handles single entity refreshes, incremental updates, and full backfills.

Orchestra launched Orchestra Runtime, a control plane for running and monitoring AI agents on secure, serverless infrastructure. It features an agent orchestrator and a context graph. Orchestra automatically creates a context graph of clean, reliable data that acts as an engineering brain.

Prefect has acquired Dagster, signaling that "orchestration alone is no longer a moat" for data infrastructure companies. This shift drives platforms toward agentic control planes. For example, Orchestra Runtime offers sandboxed infrastructure to build and run AI agents for data operations.

To classify over a million columns during a Snowflake migration, a solo engineer built a three-tier engine. It uses cheap regex rules with data-type filters for the majority of columns, reserving native Snowflake classification and language models only for ambiguous cases.

Prefect acquired rival orchestrator Dagster, shifting data pipeline tooling dynamics. Key engineering deep dives highlight Grab adopting Apache Iceberg for its data lake, Razorpay accelerating data warehouse refreshes tenfold using graph indexing, and architectures for lakehouse ACID compliance.

Prefect is acquiring Dagster Labs to combine Prefect's dynamic machine learning workflows with Dagster's asset-oriented orchestration. Dagster OSS and Dagster+ will remain active. The combined company can serve the full spectrum of orchestration needs better than either of us could alone.

Dagster AuthKit is a community authentication wrapper for self-hosted Dagster OSS that adds login interfaces, RBAC, and audit logs without modifying internal code. It solves the issue where "Dagster OSS has no auth" by wrapping the webserver command and supporting SQLite, Postgres, MySQL, and Redis.

This reference implementation demonstrates a production-shaped data pipeline running on a single 35-euro-per-month virtual server. It uses Dagster, dlt, and dbt to ingest open data into a DuckDB medallion architecture.

Edwin Weber built a metadata-driven pipeline using Dagster, dlt, dbt, and DuckDB to process Danish parliament data. He utilized Dagster assets to manage infrastructure, specifically stopping Metabase containers to prevent DuckDB locking conflicts.

Operationalizing data orchestration requires separating infrastructure from business logic through GitOps. Using tools like Dagster on Kubernetes allows for scalable, state-aware deployments. DevOps is what data engineering was a decade ago when the focus shifted to stability and automation.

DuckDB's Quack protocol introduces a client-server layer to the typically embedded engine. Benchmarking via a Dagster-orchestrated lab shows the network cost through cold-cache, reproducible experiments.

Operationalizing data orchestration requires treating the platform as a critical control plane. It emphasizes separating infrastructure from business logic through GitOps and Infrastructure as Code.