Airflow
AIRFLOW ORCHESTRATION
Design pipelines visually.
Sparkflows invokes Airflow.
Build your data and AI workflows with drag-and-drop simplicity.
Sparkflows auto-generates production-ready DAGs and invokes Airflow to run them without requiring Python.
Stop writing DAGs by hand. Let your team focus on what the pipeline does,
not how to code it.
Sparkflows sits in front of Airflow as your visual design and automation layer — so any team member can build workflows, and Airflow executes them at enterprise scale, without a single line of DAG code.
HOW IT WORKS
Three steps from idea
to running Airflow DAG
Sparkflows handles the entire journey from visual design to Airflow execution — generating, submitting, and monitoring DAGs automatically
so your team never touches the underlying code.
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01 - DESIGN
Build visually, not in code
Use Sparkflows' drag-and-drop canvas to assemble your pipeline from 400+ pre-built processors. Connect nodes to define data flow, dependencies, and logic — no Python needed.
02 - GENERATE
Auto-generate production DAGs
With one click, Sparkflows converts your visual workflow into a complete, production-ready Airflow DAG — with task dependencies, retry logic, scheduling, and operator config all included.
03 - INVOKE
Sparkflows triggers Airflow
Sparkflows submits the generated DAG to your Airflow environment and triggers execution. Airflow runs the pipeline on your Spark cluster — Sparkflows monitors progress and surfaces results.
04 - RETRY & MONITOR
Monitor & Manage Pipelines
Monitor progress, surface results, view Spark-level logs, and configure automatic retries all from a unified dashboard.
VISUAL DAG BUILDER
Anyone can build.
Airflow runs it.
Sparkflows puts a drag-and-drop canvas in front of Airflow's DAG execution engine. Business users, analysts, and engineers all work from the same visual interface — Sparkflows generates the DAG and invokes Airflow automatically in the background.
No more back-and-forth between data teams and engineering just to update a pipeline. Design, generate, invoke — all from one place.
SCHEDULING AND TRIGGERS
Run pipelines
exactly when needed
Sparkflows manages scheduling directly — configure how and when Airflow is invoked
without touching cron expressions or Airflow configuration files.
Scheduled execution
Time-based
Configure daily, hourly, or custom schedules in Sparkflows UI. Sparkflows invokes Airflow at the right time — no cron syntax required.
On-demand via API
API / Manual
Invoke any pipeline programmatically through Sparkflows' REST API — for CI/CD pipelines, app-triggered workflows, or manual ad-hoc runs.
Event-driven execution
Data-driven
Trigger pipeline runs automatically when data arrives, an upstream workflow completes, or a quality threshold is crossed.
Dependency-based chaining
Multi-pipeline
Chain multiple pipelines together, Sparkflows coordinates sequencing across workflows, triggering Airflow for each in the right order.
KEY CAPABILITIES
Built for production scale
Seven core capabilities that make Sparkflows the complete visual layer for Airflow-powered data and AI pipelines
— from design all the way to monitoring.
Visual workflow / DAG builder
Drag-and-drop pipeline design for every skill level — from business analysts to data engineers. 400+ processors cover every step of data and AI workflows.
Automated DAG code generation
Instantly converts visual workflows into production-ready Airflow DAGs — complete with dependencies, retries, and operator config. No manual authoring, no maintenance burden.
Scheduling & monitoring dashboard
Configure schedules, monitor Airflow execution status, view Spark-level logs, and receive alerts — all from a unified Sparkflows dashboard without switching tools.
Governance & role-based access (RBAC)
Ensure secure, controlled workflow execution with fine-grained access controls, audit logs, and standardised pipeline management across teams.
PLATFORM FEATURES
Everything your team needs
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Native AI & ML integration
Embed GenAI and ML models directly into your Airflow-powered pipelines. Build intelligent workflows with AI agents, LLM processors, and ML training steps — all from the visual canvas.
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GenAI & LLM processors built in
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ML training, inference & deployment nodes
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AI agents embedded into pipelines
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Multi-cloud & hybrid deployment
Deploy pipelines across AWS, GCP, Azure, Databricks, and on-premise — Sparkflows invokes Airflow on the right compute environment for each workload.
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AWS EMR, MWAA, Databricks, Dataproc
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Azure HDInsight & on-premise Spark
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YARN and Kubernetes supported
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Built-in analytics & visualisation
Create dashboards, charts, and reports directly within Sparkflows pipelines. Combine data engineering and analytics in a single workflow — no separate BI tool required.
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Time-based scheduling
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Data-driven execution
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Dependency-based triggers
BUSINESS IMPACT
Quantifiable gains from
visual orchestration
10x
Faster pipeline development
Drag-and-drop design and reusable components cut weeks of engineering work down to hours.
30%
Improvement in operational efficiency
Understand margin sacrificed during promotions and whether discount-driven customers come back at full price.
40-60%
Reduction in maintenance effort
Auto-generated DAGs eliminate manual coding and ongoing upkeep as pipelines evolve.
60-80%
Reduced engineering dependency
Self-service capabilities let business users and analysts build workflows without waiting on data engineering.
50%
Faster time to production
Accelerate deployment of data and AI workflows from design to live Airflow execution.
70-90%
Improved ROI from AI initiatives
Faster experimentation, deployment, and scaling of ML and GenAI use cases across the enterprise.
WHY SPARKFLOWS
Visual simplicity.
Airflow reliability.
No more hand-written DAGs
Every DAG in Airflow requires manual Python development — complex to write, harder to maintain as pipelines grow. Sparkflows eliminates that entirely. Design visually, get a production DAG automatically.
Keep the power of Airflow
Sparkflows doesn't replace Airflow — it makes it accessible. You keep Airflow's scheduling reliability, executor ecosystem, and enterprise maturity. You just never have to write a DAG again.
Democratise workflow ownership
With traditional Airflow, only engineers can build and modify pipelines. Sparkflows opens that up to analysts, scientists, and business users — reducing the bottleneck on engineering teams.
One platform for data + AI
ETL, ML training, GenAI, analytics, and delivery — all designed in one visual canvas, all invoked through Airflow. No fragmented toolchain, no context switching, no separate orchestration layer.
USE CASES
What teams build
with Sparkflows
From daily ETL to enterprise-scale AI pipelines — Sparkflows generates the DAG, Airflow runs it.
Data pipeline automation
Supply chain orchestration
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AI / ML & GenAI workflows
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Customer analytics pipelines
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Financial data processing & reporting
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Change data capture (CDC)
Real-time streaming analytics