The data platform that keeps pace with your team
Query, govern, and version your entire data estate from a single SQL interface. Built for teams that ship fast and never break prod.
// trusted by 200+ data teams worldwide
// core capabilities
Everything you need, nothing you don't
Query Engine
Federated SQL across every source — warehouses, databases, object storage — without moving data.
Data Catalog
Automatic schema discovery with column-level lineage, profiling, and full-text search across all assets.
Pipeline Orchestration
Visual DAG editor with 50+ connectors, cron and event scheduling, and real-time run monitoring.
Governance & RBAC
Row-level security, dynamic data masking, PII detection, and complete audit trails for every change.
Observability
Continuous data quality scoring, anomaly detection, SLA tracking, and pipeline health dashboards.
Git-like Branching
Branch, commit, and merge your data schemas. Isolated experiments with atomic, auditable merges.
// pipeline orchestration
ETL that builds itself
Connect any source, transform with SQL or Python, and land clean data in your Iceberg lakehouse — all from a visual interface.
PostgreSQL
Source
REST API
Source
Kafka
Source
Filter
Transform
Join
Transform
Aggregate
Transform
Iceberg Lake
Destination
Dashboard
Destination
Visual DAG editor
Drag-and-drop pipeline builder with 50+ pre-built connectors and transforms.
Real-time monitoring
Live run tracking, row-level metrics, error alerting, and SLA dashboards.
Event & cron scheduling
Trigger pipelines on schedule, webhook, or data-change events with retry policies.
// Developer Experience
Built for developers
-- federated join across two sourcesWITH recent_events AS (SELECT user_id, event_type, tsFROM lake.events@mainWHERE dt = '2026-04-09')SELECT c.region, COUNT(*) AS hitsFROM recent_events eJOIN postgres.crm.customers cON c.id = e.user_idGROUP BY c.region
from dxdata import Clientclient = Client(api_key="your-api-key")# Execute a SQL queryresult = client.query(sql="SELECT * FROM sales.orders LIMIT 10")# Access results as a DataFramedf = result.to_pandas()print(f"{result.stats.row_count} rows in {result.stats.duration}")
curl -X POST https://api.dxdata.dev/v1/query \-H "Authorization: Bearer $DXDATA_API_KEY" \-H "Content-Type: application/json" \-d '{"sql": "SELECT region, COUNT(*) FROM events GROUP BY region","branch": "main","format": "json"}'
import { DXData } from "@dxdata/client";const client = new DXData({apiKey: process.env.DXDATA_API_KEY,});// Query with full type safetyconst result = await client.query({sql: "SELECT * FROM sales.orders LIMIT 10",});console.log(`${result.stats.rowCount} rows in ${result.stats.duration}`);
// versioned.catalog
Branch your data like you branch your code
Create isolated data branches, run experiments, and merge changes atomically. Every commit is versioned, every change is auditable, every state is time-travelable.
// integrations
Connect everything
Federate across 50+ data sources — warehouses, databases, streams, and APIs — without moving a single byte.
// why teams switch
See how DXData compares
One platform replaces four — with better performance, built-in governance, and predictable pricing.
| Feature | RecommendedDXData | Snowflake | Databricks | BigQuery |
|---|---|---|---|---|
Query Speed | Sub-second | Seconds | Seconds | Seconds |
DXData uses columnar storage with vectorized execution to deliver sub-second responses on datasets up to 10 TB. | ||||
Concurrent Workloads | Auto-isolated | Warehouse scaling | Cluster scaling | Slot-based |
Automatic workload isolation ensures analytics and ingestion never compete for resources. | ||||
Branching | ||||
Git-like branching for your data — create isolated environments for testing schema changes without copying data. | ||||
Open Standards | Partial | |||
First-class Apache Iceberg and Parquet support — no vendor lock-in, full interoperability with the open data ecosystem. | ||||
Built-in Governance | Built-in | Add-on | Unity Catalog | Partial |
Column-level access control, data lineage, and automated compliance policies included out of the box. | ||||
Audit Trail | Immutable | Basic | Configurable | Basic |
Immutable, queryable audit logs for every data access, schema change, and permission update. | ||||
Pricing Model | Predictable | Per-credit | Per-DBU | Per-query |
Flat, predictable pricing based on storage and seats — no surprise bills from runaway queries. | ||||
Setup Time | Minutes | Hours | Days | Hours |
From sign-up to first query in under 5 minutes. No infrastructure provisioning, no cluster management. | ||||
categories where DXData leads
lower total cost of ownership
// pricing
Simple, transparent pricing
Start free. Scale when you need to. No hidden fees.
Free
For individuals exploring data workflows
Free forever
- Up to 3 data sources
- 1,000 queries per month
- Basic SQL editor
- Community support
- 1 user seat
- 5 GB storage
- Core data catalog
- Standard connectors
Pro
For teams building production data pipelines
per user, billed annually
- Unlimited data sources
- Unlimited queries
- Full SQL Analytics Studio
- Pipeline orchestration
- Shared worksheets & notebooks
- Team collaboration (up to 25)
- 100 GB storage
- Priority email support
- Data governance basics
- API access
Enterprise
For organizations with advanced security needs
Talk to us for pricing
- Everything in Pro, plus:
- Unlimited users & pipelines
- SSO (SAML, OIDC)
- Advanced audit logs
- Custom SLA (99.99% uptime)
- Dedicated support engineer
- Custom integrations
- HIPAA, SOC 2 Type II, GDPR
- Unlimited storage
// faq
Frequently asked questions
// security
Enterprise-grade security
Your data is protected by industry-leading standards, independently audited, and encrypted end-to-end. We take security seriously so you can focus on your data.
// get started
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Uptime SLA
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p95 latency
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