Data Pipelines That Don't Break Silently.
We build asset-centric pipelines with Dagster—type-safe, testable, and observable. No more 3 AM alerts from brittle Airflow DAGs.
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Enterprise-Grade Engineering
Data Pipelines built on the same rigorous principles that define all NodesAI systems—reliability you can stake your operations on.
State-of-the-Art Technology
Dagster for orchestration, Polars for high-performance transforms, and typed Python throughout. We use what leading data teams are adopting—not legacy Airflow.
Data Quality First
Every asset includes validation gates. Pydantic schemas catch errors at parse time. Asset checks verify business rules post-load. Bad data gets blocked, not propagated.
Maintainable Code
Type-safe Python with strict separation: business logic stays clean, infrastructure is injected. Your team can extend and debug our code without reverse-engineering it.
Intelligent Orchestration
Dagster manages asset dependencies automatically—with scheduling, retries, sensors, and full observability into why each materialization happened.
Why Your Current Pipeline Architecture Fails
Silent Failures
Task-based systems like Airflow report "success" even when data quality is garbage. You only discover problems when dashboards break.
Brittle Dependencies
Hardcoded paths, implicit contracts, and scattered configuration make every change a potential landmine.
No Observability
When something fails, you can't trace which upstream asset caused it or what data was processed at that moment.
The "What" vs "The How" Architecture
We enforce strict separation between business logic (assets) and infrastructure (resources, I/O managers, partitions). Your transformation code stays clean, testable, and deployment-agnostic.
Assets declare what data they produce and consume—Dagster handles scheduling, retries, and lineage tracking automatically. No more imperative task orchestration.
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Asset-Centric Pipeline Benefits
Software-Defined Assets
Assets declare their dependencies declaratively. Dagster builds the DAG automatically—no manual wiring.
Resource Injection
Database connections, API clients, and secrets injected cleanly. Swap local mocks for production with zero code changes.
I/O Managers
Parquet, JSONL, database tables—assets don't care where data lives. I/O managers handle persistence with O(1) memory.
Partitions
Time-based, categorical, or dynamic partitions. Process decades of data with parallel execution across partitions.
Asset Checks
Data quality gates run post-materialization. Catch schema drift, null explosions, and business rule violations automatically.
Full Lineage
Track every asset's upstream dependencies, materialization history, and downstream consumers in one UI.
How Dagster Pipelines Work
Discovery
Ingest from APIs, databases, or files with partitioned parallel execution.
Enrichment
Join, transform, and validate data with Polars LazyFrames for memory efficiency.
Validation
Pydantic models enforce schemas. Asset checks verify business rules post-load.
Delivery
Load to Neo4j, Postgres, Snowflake, or vector stores with typed I/O managers.
Production-Grade Stack
⚙️ Dagster
Asset-centric orchestration with built-in scheduling, retries, sensors, and a powerful development UI.
🐻❄️ Polars
High-performance DataFrame library. LazyFrames enable streaming sinks for O(1) memory on billion-row datasets.
🔒 Pydantic
Type-safe configuration and data validation. Catch schema errors at parse time, not in production.
🐳 Docker
Containerized deployments for reproducible environments from local development to cloud production.
Industry Applications
Manufacturing
Ingest sensor data, maintenance logs, and production metrics into unified analytics pipelines.
Pharmaceutical
Build audit-ready pipelines for clinical trial data with full lineage and validation gates.
Mining & Energy
Process geological surveys, IoT sensors, and operational data for predictive maintenance.
Financial Services
Compliance-ready ETL with data lineage, schema enforcement, and reproducible transformations.
AI/ML Teams
Feature pipelines, embedding generation, and RAG data preparation with vector store integration.
Analytics
Replace brittle cron jobs with observable, testable pipelines feeding your BI tools.
The Surgeon, Not the General Practitioner
We don't build "data MVPs." We rescue failing ETL systems by re-architecting with asset-centric pipelines and type-safe validation. If your current system breaks silently, lacks lineage, or can't scale—that's exactly where we start.
Every engagement has defined scope and deliverables. You're not buying hours of coding; you're buying the absence of risk. We've built enough pipelines to know exactly where the problems hide.
About NodesAIWhat's Included
✓ Included
- Pipeline architecture design
- Dagster project setup and configuration
- Asset definitions with dependency graphs
- Custom I/O managers (Parquet, JSONL, DB)
- Partition strategies (time, categorical)
- Asset checks for data quality
- Resource factories with secrets management
- Docker containerization
- Documentation and training
- 30-day post-launch support
✗ Not Included
- Source data cleaning and preparation
- Cloud infrastructure provisioning
- Ongoing hosting and compute costs
- Real-time streaming (Kafka, Kinesis)
- Legacy system reverse engineering
- BI dashboard development
Engagement Options
Diagnosis
2-day engagement
- Current architecture review
- Data source inventory
- Pipeline design proposal
- Technology recommendations
- Effort estimation
Implementation
3-5 week project
- Everything in Diagnosis
- Full Dagster pipeline build
- Custom I/O managers
- Asset checks and validation
- Docker deployment
- 30-day support included
Maintenance
per month
- Pipeline monitoring
- Performance optimization
- New asset development
- Schema evolution support
- Priority support
Frequently Asked Questions
Why Dagster instead of Airflow?
Airflow is task-based, meaning it focuses on "running tasks." Dagster is asset-centric, meaning it focuses on "producing data." This difference leads to better testability, observability, and data quality management.
Does this replace my existing data warehouse?
No, Dagster orchestrates the movement and transformation of data into your warehouse (Snowflake, BigQuery, Databricks). It acts as the control plane for your data infrastructure.
How do you handle data quality?
We use Pydantic for schema validation at runtime and Dagster Asset Checks to verify data quality rules (e.g., "no nulls in column X") immediately after data is produced.
Can I run this on-premise?
Yes, Dagster and our pipeline architecture can be deployed on-premise using Docker and Kubernetes, ensuring your data never leaves your secure environment.
Ready to Build Pipelines That Actually Work?
Let's discuss how Dagster can replace your brittle ETL with production-grade data infrastructure.
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