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Data warehouse ⇄ CRM

Apache Druid to Vitally integration — real-time, two-way sync

Keep Apache Druid and Vitally in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Apache Druid and Vitally

Sync Vitally into Apache Druid continuously and push warehouse results back onto CRM records, one two-way connection instead of two pipelines.

The CRM feeds the warehouse and the warehouse should feed the CRM: relationship data flows one way, and computed scores, segments, and customer context flow back. Most teams build the first half as a batch pipeline and never quite get to the second.

Stacksync does both with one connection. NPS Response, Custom Trait, Account, User from Vitally land in Apache Druid as live tables, updated within seconds, and columns computed in Apache Druid write back to fields in Vitally. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Keep CS tasks and notes aligned between Vitally and ticketing or project tools.
  • 02 Sync account health scores and lifecycle stages from Vitally into a CRM so sales sees churn risk before renewals.
  • 03 Keep lookup tables in Druid refreshed from a CRM or database so query-time joins use current reference data.
  • 04 Expose product telemetry stored in Druid to business tools without granting direct cluster access.

Common sync patterns

Cleanup that sticks

Deduplication and normalization done in Apache Druid can be written back, so warehouse-side cleanup actually fixes the CRM.

CRM analytics on live data

Accounts, contacts, and activity from Vitally are queryable in Apache Druid moments after they change, so dashboards stop lagging the reality they describe.

Scores and segments back on the record

Lead scores, churn risk, or usage segments computed in Apache Druid appear as fields in Vitally, where the people working accounts actually see them.

What you can sync between Apache Druid and Vitally

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Apache Druid objects Vitally objects How this pairing syncs
Tasks Batch ingestion and compaction jobs monitored during data loads. Task CS tasks and follow-ups, readable and writable for workflow sync. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Lookups Key-value mappings joined at query time, refreshable from external systems. User End users tied to accounts, including activity and custom traits. Lookups is specific to Apache Druid and User to Vitally — each maps to any object or custom field on the other side.
Datasources The table-like unit of storage and querying, the main target of reads and ingestion. Organization Parent organizations for hierarchical B2B account structures. Datasources is specific to Apache Druid and Organization to Vitally — each maps to any object or custom field on the other side.
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Note Account and user notes captured by success teams. Segments is specific to Apache Druid and Note to Vitally — each maps to any object or custom field on the other side.
Dimensions String and categorical columns used for filtering and grouping in synced queries. Conversation Customer conversations logged in Vitally; activity objects include parent object details in the payload. Dimensions is specific to Apache Druid and Conversation to Vitally — each maps to any object or custom field on the other side.
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. NPS Response NPS survey responses for account-health reporting. Metrics is specific to Apache Druid and NPS Response to Vitally — each maps to any object or custom field on the other side.

How changes propagate between Apache Druid and Vitally

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Apache Druid Vitally Interval-based propagation

DetectionStacksync polls Apache Druid for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Druid through streaming or batch ingestion rather than row updates.

DeliveryEach detected change is written to Vitally through its API, with automatic retries and rate-limit backoff.

Vitally Apache Druid Sub-second propagation

DetectionVitally notifies Stacksync of record changes through webhook events. Incremental polling on updatedAt cursors.

DeliveryEach detected change is applied to Apache Druid as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Druid: No fixed API quotas; query concurrency is bounded by broker and historical node capacity.
  • Vitally: Default rate limit of 1,000 requests/min (token bucket); write operations consume more budget, headers expose remaining quota.
What ships with Apache Druid ⇄ Vitally

Connect Apache Druid and Vitally for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Vitally connection.

Real-time

Two-way sync

Changes in Apache Druid or Vitally instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or Vitally data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Apache Druid or Vitally record.

Observability

Monitoring

Track your Apache Druid ⇄ Vitally sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Druid and Vitally.

How the Apache Druid and Vitally connectors work

Apache Druid

Integration surface
REST API (SQL over HTTP and native JSON queries); JDBC via Avatica
Authentication
Deployment-dependent: basic authentication or an authenticator extension; often fronted by a proxy
Change detection
Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates
Capabilities
read · write
Rate limits
No fixed API quotas; query concurrency is bounded by broker and historical node capacity

Vitally

Integration surface
REST API with cursor-based pagination (sortable by createdAt/updatedAt)
Authentication
API key via Basic Auth; keys created in Settings -> Integrations -> REST API and individually revocable
Change detection
Incremental polling on updatedAt cursors; playbook-triggered webhooks can push events for near real-time updates
Capabilities
read · write · webhooks
Rate limits
Default rate limit of 1,000 requests/min (token bucket); write operations consume more budget, headers expose remaining quota.
How it works

How to connect Apache Druid to Vitally — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Apache Druid and Vitally with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Apache Druid connected
    Vitally connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Druid and Vitally objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Apache Druid ⇄ Vitally
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Apache Druid Vitally
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and Vitally integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 390 integrations available for Apache Druid and Vitally.

Popular · 8 of 390
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