Product
Data infrastructure that serves your business, not just your data team
If the CEO needs an analyst to get a number, your data infrastructure has failed. We fix that.
2.4 PB
Active data under management
< 200ms
P99 query on 12-month history
2M/sec
Peak event ingestion
80+
Production-certified connectors
Capabilities
A data stack that every team can actually use
Engineers get a full-featured query engine. Analysts get a no-code builder. Executives get dashboards that are never stale. Built on the same substrate — no duplication, no divergence.
Real-time streaming ingestion
Process up to 2 million events per second per tenant with sub-100ms end-to-end latency from source to query. No batching, no backpressure, no silent data loss. Every event is acknowledged before we confirm receipt.
2M events/sec · < 100ms E2E latency
Tiered storage — 2.4 PB active
Hot, warm, and cold tiers managed automatically based on access frequency and your cost targets. Query data that's 3 years old as fast as data from yesterday. Transparent compression — 8:1 average ratio.
< 200ms P99 query on 12-month history
No-code pipeline builder
Business analysts build transformation pipelines without writing SQL or Python. Engineers keep full access to the underlying execution engine. Both audiences work from the same data without separate tooling.
60% reduction in data request backlog
Unified query layer
One query interface across logs, metrics, traces, events, and relational data. Correlate infrastructure performance with business outcomes in a single query. No join complexity — the query planner handles it.
Unified query across 40+ data source types
Full data trail and history
Every metric knows exactly where it came from, what changes it passed through, who approved those changes, and when. Required for financial, healthcare, and regulatory audit programs.
Full data lineage · complete audit trail
Row-level and column-level security
Data access policies are enforced at the query engine layer — not in the application. Users see exactly the data they're authorized for, regardless of which tool they're using to query it.
Role-based access control · policy enforcement
80+ native connectors
Certified connectors for cloud data warehouses, operational databases, SaaS platforms, IoT streams, and BI tools. Maintained by our engineering team — not community-contributed and left to rot.
80+ connectors · All production-tested
Data across multiple regions
Data shared across regions with consistent access rules and full tracking. Each team owns their data. Central standards are enforced automatically without creating a bottleneck.
Distributed data architecture with central governance
Push data back into your business tools
Send analysis results back into your CRM, ERP, or marketing platforms on any schedule. Connect insights to action without building custom pipelines from scratch.
Push to 40+ business system destinations
Architecture
Five layers. One stack. No vendor proliferation.
Most enterprise data stacks are assembled from 10–15 separate tools. Every boundary between tools is a place where data can be lost, delayed, or corrupted. We eliminate those boundaries.
Consumption
Every consumption method hits the same query engine with the same access controls.
Semantic layer
The single source of truth for what metrics mean — enforced everywhere.
Processing
Real-time and batch in one engine. No pipeline proliferation.
Storage
Tier selection is automatic. You query, we optimize.
Ingestion
Ingest from any source. Every event acknowledged on receipt.
By role
Every team gets what they actually need
Data Engineers
- Full SQL + Python access to the execution engine
- dbt-compatible transformation layer
- Git-based pipeline versioning
- Automated data quality tests with alerting
- Performance profiling and cost attribution
- Custom connector SDK for proprietary sources
Business Analysts
- No-code pipeline builder with visual lineage
- Self-service metric definition with governance guardrails
- Drag-and-drop dashboard builder
- Natural language query (NLQ) for ad hoc exploration
- Scheduled reports with automated distribution
- Alerting on metric thresholds — no engineering required
Executives & Finance
- Pre-built executive dashboards refreshed every 8 seconds
- Mobile app for iOS and Android
- AI-generated natural language summaries of changes
- Anomaly highlighting with context, not just alerts
- Cross-functional scorecards with drill-down
- Board-ready export in one click
Industry use cases
What enterprises actually use it for
Financial Services
Real-time fraud detection at 400,000 transactions/sec
31% reduction in annual fraud losses
A Tier-1 bank replaced 14 separate monitoring tools with the Analytics Suite. Fraud detection latency dropped from 340ms to 12ms. Annual fraud losses fell 31% in the first year. The risk team now queries events from any of the last 7 years in under 200ms.
Healthcare
Clinical operations dashboard across 240 hospital sites
48-hour data lag → 8-second freshness
A national health system replaced a reporting workflow that required a 48-hour data delay with real-time dashboards refreshed every 8 seconds. Administrators act on current capacity data instead of yesterday's. Staffing decisions are made with actual demand data.
Manufacturing
Predictive maintenance across 12,000 IoT sensors
43% reduction in unplanned downtime
An automotive manufacturer routes sensor telemetry from 12,000 production floor sensors through the Analytics Suite. Anomaly detection models run on the stream in real time. Unplanned downtime fell 43% in the first year.
Technical specifications
The numbers your data engineers will ask for
| Ingestion throughput | 2M events/sec per tenant (burst: 4M/sec) |
| Streaming latency | < 100ms source to queryable (P99) |
| Hot query latency | < 200ms P99 for last 12 months |
| Storage capacity | 2.4 PB total, auto-tiered |
| Compression ratio | 8:1 average (Zstandard) |
| Data retention | Configurable — 90 days to indefinite |
| Query API | REST, GraphQL, JDBC/ODBC, Postgres wire protocol |
| Auth & access control | RBAC + ABAC, row-level + column-level security |
| Compliance | SOC 2 Type II, HIPAA, GDPR, FINRA, BCBS 239 |
| Uptime SLA | 99.99% for query API, 99.95% for streaming ingest |
| Source connectors | 80+ production-certified, custom SDK available |
| Destination connectors | 40+ reverse ETL destinations |
Connects to everything already in your stack
Common questions
What data teams ask before they sign
Can we migrate from our existing Snowflake/Databricks setup?
Yes. We provide a migration tool that handles schema migration, historical data transfer, and query translation. Most migrations run in parallel — your existing stack stays live until you're satisfied the new environment is producing identical results.
How does row-level security work across different consumption tools?
Access policies are defined once in the semantic layer and enforced at the query execution layer — before results are returned. Regardless of whether a user queries via SQL client, BI tool, API, or the embedded SDK, the same policy applies. There's no per-tool configuration.
Is it compatible with our existing dbt models?
Yes. The transformation layer is dbt-compatible — you can run your existing dbt project unchanged. We also provide an enhanced execution environment that adds features like automated quality tests, cost attribution per model, and column-level lineage.
How do you handle personally identifiable information (PII)?
PII discovery runs automatically on ingestion and tags fields with a sensitivity classification. Column-level security policies can mask, tokenize, or restrict PII at the query layer. Your GDPR and CCPA right-to-erasure workflows are supported natively.
What's the proof of concept process?
We stand up a dedicated proof-of-concept environment with your actual data (a representative sample you provide) within 48 hours of a signed PoC agreement. The PoC runs for 30 days with full support. You keep the migration and setup work regardless of whether you proceed.
See how fast your team can go from raw data to decisions
We'll run a live proof of concept on your own data. Typical setup: 48 hours. Typical timeline to first insight: day 3.