Datablast for Data Engineers
Build in hours what used to take sprints.
Flare generates and modifies workflows from plain language — you review every line. Orchestration, quality checks, and lineage come built in, so you maintain systems instead of glue.
The problem
Sound familiar?
The stack is the job
Ingestion, orchestration, transformation, quality, and BI each have their own tool, auth, and failure modes. You maintain integrations instead of building.
Ad-hoc requests never stop
Every team needs "one quick number". Context-switching to serve them costs the roadmap.
Nobody trusts the dashboards but everyone blames you
Silent schema changes and upstream breakages surface as wrong numbers in an exec meeting.
What teams build
Data Engineers use cases on Datablast
Flare-generated workflows
Describe the pipeline; review the generated code; ship. Modifications work the same way. Your code and data stay private.
Built-in orchestration and monitoring
Scheduling, retries, dependency tracking, and alerting without running your own Airflow.
Data quality as a default
Freshness, volume, and schema checks attach to every model — issues are caught before they reach reports.
Lineage and dependency tracing
See how workflows, scripts, and dashboards connect. Know the blast radius before you change anything.
Self-serve that actually reduces tickets
Business teams ask Flare instead of you. Governed definitions mean their answers match yours.
Hours
from request to production pipeline
Fewer
ad-hoc tickets — Flare handles them
Caught
data issues before dashboards break
Works with your stack
Ready to build a data foundation your whole team can trust?
See how Datablast and Flare work together — in a walkthrough built around your stack.