Your data. Your pipeline.
One platform.
Pulse replaces fragmented data stacks — brittle Airflow DAGs, scattered dbt projects, and missing observability — with a unified platform built for data teams who need reliability at scale.
Data ingestion shouldn't be a daily firefight.
Before you can transform or analyze, you need reliable ingestion. But most teams are stuck with fragile connectors that break silently, batch jobs that miss SLAs, and real-time streams that lag without warning.
- ✕Airflow DAGs fail silently — data stops flowing but alerts only trigger after downstream breakage
- ✕Schema changes in source systems break pipelines weekly, requiring manual restarts
- ✕Real-time streams and batch jobs run on separate systems with no unified view
- ✕No backpressure handling — source systems overwhelm downstream during peak hours
- ✓Automatic circuit breakers pause ingestion on anomaly detection, preventing cascade failures
- ✓Schema registry with drift detection — breaking changes caught before they hit production
- ✓Unified streaming and batch interface — one config, both modes, seamless switching
- ✓Intelligent backpressure — queues auto-scale and throttle to protect downstream systems
Transform data without the 3 AM page.
Your analysts are waiting. Your dbt runs are failing. Your SQL is scattered across five different tools, and nobody knows which model is the source of truth.
- ✕dbt runs take 4 hours — the overnight job fails, and morning reports are stale
- ✕No lineage visibility — changing one model breaks three downstream dashboards
- ✕SQL duplicated across Looker, Mode, and Airflow — three versions of "active users"
- ✕Data quality checks are manual — bad data reaches executives before anyone notices
- ✓Incremental processing with intelligent partitioning — 4-hour runs become 12 minutes
- ✓Automatic column-level lineage — see every upstream and downstream dependency
- ✓Single source of truth — version-controlled SQL accessible across all tools
- ✓Real-time quality gates — data never flows downstream without passing checks
Bad data is worse than no data.
Data quality failures don't announce themselves. They show up as confused executives, broken dashboards, and analysts spending Friday nights hunting for why the numbers don't match.
47% of data teams report making business decisions on incorrect data at least monthly. The average time to detect a data quality issue in traditional stacks is 4.2 days.
- ✕Data quality checks are manual SQL queries run after the pipeline completes
- ✕No unified quality dashboard — different teams use different tools
- ✕Alerts fire on technical failures but miss semantic issues (negative revenue, impossible dates)
- ✕Root cause analysis requires digging through logs across multiple systems
- ✓Automated quality gates at every pipeline stage — bad data never reaches production
- ✓Unified quality scorecard with lineage-aware impact analysis
- ✓Semantic rule engine — detect business-logic violations, not just technical failures
- ✓One-click root cause analysis — trace any issue to its source in seconds
See how Pulse prevents bad data from reaching your stakeholders — automatic validation that scales with your pipeline.
Deploy with confidence, not hope.
Production deployments are where data pipelines earn their reputation. One bad rollout can corrupt downstream datasets, break dashboards, and leave your team explaining to stakeholders why the numbers are wrong.
- ✕Blue-green deployments require manual orchestration across multiple tools
- ✕Rollback takes 20+ minutes — long enough for bad data to reach stakeholders
- ✕No deployment history — impossible to know what changed and when
- ✕Production access requires SSH into servers — no audit trail, no safety
- ✓One-click blue-green deployments with automatic traffic switching
- ✓Instant rollback — revert to any previous version in under 30 seconds
- ✓Complete deployment audit log — every change tracked and reversible
- ✓Role-based access with approval workflows — deploy safely from the UI
We migrated 200+ pipelines to Pulse and reduced our deployment failures by 94%. The instant rollback feature alone has saved us countless hours.
You can't fix what you can't see.
The costliest data issues are the ones you never notice — gradual drift, silent failures, and slow degradation that compounds over weeks until your metrics are meaningless.
- ✕Logs scattered across CloudWatch, DataDog, and Splunk — no unified view
- ✕Alerts based on static thresholds miss gradual degradation patterns
- ✕Dashboards require manual updates when pipeline structure changes
- ✕No cost visibility — surprised by the warehouse bill at month-end
- ✓Unified observability — logs, metrics, and lineage in one view
- ✓ML-powered anomaly detection learns your baselines automatically
- ✓Auto-updating dashboards reflect pipeline changes in real-time
- ✓Per-query cost attribution and optimization recommendations built-in
Ready to build pipelines that just work?
Start building with Pulse today. Free for small teams, scales with your growth. No credit card required to get started.
Ready to modernize your stack? Let's talk.
Three quick questions help us tailor your demo to your actual data infrastructure. No generic product tours — just relevant answers to your specific challenges.
Migrating from Airflow? Here's the playbook.
The Pulse Migration Playbook is a comprehensive guide for teams currently running Airflow, dbt, or custom Python stacks. Covers DAG migration, team onboarding, parallel operation during transition, and cost optimization strategies.
- ›Airflow to Pulse migration checklist
- ›DAG dependency mapping guide
- ›Schema migration templates
- ›Parallel ops runbook for active pipelines
- ›Cost comparison framework