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Data warehouse ⇄ CRM

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

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

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  • 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 Freshworks CRM

Sync Freshworks CRM 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. Lists, Notes, Contacts, Accounts from Freshworks CRM land in Apache Druid as live tables, updated within seconds, and columns computed in Apache Druid write back to fields in Freshworks CRM. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • Two-way sync accounts and deals with an ERP so quotes and invoices reference the same customer records.
  • Keep list membership aligned with segments computed in the warehouse.
  • Keep lookup tables in Druid refreshed from a CRM or database so query-time joins use current reference data.
  • Expose product telemetry stored in Druid to business tools without granting direct cluster access.

Scores and segments back on the record

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

A single customer view

Join Freshworks CRM's relationship data with billing, product, and support data in Apache Druid to build the customer picture the CRM alone cannot hold.

Cleanup that sticks

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

What you can sync between Apache Druid and Freshworks CRM

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 Freshworks CRM objects
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Accounts Company records kept consistent with ERP and billing systems.
Dimensions String and categorical columns used for filtering and grouping in synced queries. Deals Pipeline records synced out for forecasting and full-funnel reporting.
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. Tasks Follow-ups created from external signals such as product usage events.
Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. Appointments Meeting records readable for activity reporting.
Lookups Key-value mappings joined at query time, refreshable from external systems. Sales activities Logged activity types used in engagement and productivity analysis.
Tasks Batch ingestion and compaction jobs monitored during data loads. Lists Contact list membership synced against segments computed in a warehouse.
What ships with Apache Druid ⇄ Freshworks CRM

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or Freshworks CRM 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 Freshworks CRM record.

Observability

Monitoring

Track your Apache Druid ⇄ Freshworks CRM 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 Freshworks CRM.

How the Apache Druid and Freshworks CRM 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

Freshworks CRM

Integration surface
REST API
Authentication
API key sent as a Token authorization header
Change detection
Polling with updated-at filters; outbound webhooks can be configured through workflow automations
Capabilities
read · write · webhooks
Rate limits
Subject to per-account API rate limits that vary by plan
How it works

How to connect Apache Druid to Freshworks CRM — 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 Freshworks CRM 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
    Freshworks CRM connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Druid and Freshworks CRM 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 ⇄ Freshworks CRM
    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 Freshworks CRM
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and Freshworks CRM integration FAQ

SECURITY

Security teams love 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.

Learn more about security
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ISO 27001
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GDPR
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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 386 integrations available for Apache Druid and Freshworks CRM.

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