Data engineering
Batch and streaming pipelines, warehouse modelling and data-quality enforcement.
Conceptual visualization only
Overview
Pipelines that are observable and testable: declared sources, idempotent loads, versioned transformations and quality gates that stop bad data before it reaches a dashboard or a model.
Problems it solves
- • Reports disagreeing because every team transforms data differently
- • Pipelines that fail silently and backfill incorrectly
- • No lineage, so nobody can trace a number to its source
- • Streaming and batch paths producing different answers
Who it suits
- • Analytics engineering teams
- • Data platform owners
- • Operations teams consolidating systems
What this capability does
- • Source-to-warehouse pipeline design
- • Orchestration, retries and idempotent loads
- • Dimensional and semantic modelling
- • Streaming ingestion and change data capture
- • Data quality tests, lineage and alerting
How This AI Agent Works
The delivery process for data engineering: trigger, validation, approved tooling, human approval and the deliverable produced.
Data engineering workflow
From source data to a published, monitored dataset.
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
Workflow summary as a text list
- 1. Source data: Trigger. Connect the approved source systems and extracts.
- 2. Ingest: Action. Land raw data with lineage and run metadata.
- 3. Validate: Validation. Apply schema, freshness and volume expectations.
- 4. Transform: Approved tool. Build the modelled tables from the raw layer.
- 5. Quality check: Validation. Run the agreed quality rules and record failures.
- 6. Publish approval: Human approval. A data owner approves the first publication. Human approval required.
- 7. Load and publish: Action. Load the curated tables and expose them.
- 8. Monitor: Monitoring. Track pipeline health, freshness and rule failures.
- 9. Curated dataset: Output. Documented tables, lineage and quality report.
Approval gate: Publish approval. Deliverable: Dataset.
Workflow audit for this page
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| data-engineering | 9 | Yes. Policy: always | Dataset | Available | Available (signed in) | Available via Studio · requires ai_provider | Not yet covered |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Supported industries
- Utilities
- Retail and e-commerce
- Energy
- Oil and gas
- Transportation and logistics
Technology stack
Every supported technology for this capability.
- Apache Spark
- Dask
- Airflow
- Kafka
- dbt
- ETL/ELT
- Data lakes
- Data warehouses
- Streaming pipelines
- Data quality
Specialist AI agents
Data engineering agent
Pipeline and model design
Profile not published — this role runs inside the capability workflow.
Pipeline agent
Orchestration and scheduling plan
Profile not published — this role runs inside the capability workflow.
Data quality agent
Quality rules, profiling and anomaly checks
Open agent profileAnalytics engineering agent
Semantic layer and metric definitions
Profile not published — this role runs inside the capability workflow.
Workflow
Every run follows the same auditable spine.
- Step 1
Request
You describe the objective, constraints and delivery format.
- Step 2
Validate inputs
Files, schemas, credentials and scope are checked before any agent starts.
- Step 3
Assign agent
The registry resolves the specialist agent and its permitted tools server-side.
- Step 4
Use approved tools
Only tools allowed for that agent and your plan can execute.
- Step 5
Process data
The agent designs the pipeline stages, transformations and quality rules.
- Step 6
Validate output
Quality expectations and lineage coverage are checked before delivery.
- Step 7
Human review if required
Sensitive outputs are withheld until an approver releases them.
- Step 8
Deliver result
Approved deliverables are released to your workspace with an export trail.
Inputs and outputs
What you provide
- • Source system inventory and sample extracts
- • Volume, frequency and latency targets
- • Warehouse or lake platform details
- • Business metric definitions
What you receive
- • Pipeline architecture and stage list
- • Transformation and model specifications
- • Data-quality test suite
- • Orchestration and alerting plan
Use cases
- Consolidating ERP, CRM and sensor data into one warehouse
- Streaming telemetry pipeline feeding operational dashboards
- Data-quality gate blocking bad loads before publication
Integration status
Real configuration states recorded by health checks — nothing here is assumed to be connected.
- AI providerSign in to view status
The model gateway every agent run and consultant reply depends on.
- DatabaseSign in to view status
Run persistence, agent registry, approvals, usage and audit records.
- File storageSign in to view status
Requirement-file uploads and generated artifact storage.