Real-time
Two-way sync
Changes in Apache Druid or Freshworks CRM instantly reflect in both systems. No stale data, no manual imports.
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.
- 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
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
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
- 01 Two-way sync accounts and deals with an ERP so quotes and invoices reference the same customer records.
- 02 Keep list membership aligned with segments computed in the warehouse.
- 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
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 | How this pairing syncs | |
|---|---|---|---|
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Tasks Follow-ups created from external signals such as product usage events. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Sales activities Logged activity types used in engagement and productivity analysis. | Ingestion Supervisors is specific to Apache Druid and Sales activities to Freshworks CRM — each maps to any object or custom field on the other side. | |
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Lists Contact list membership synced against segments computed in a warehouse. | Lookups is specific to Apache Druid and Lists to Freshworks CRM — 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. | Notes Context records attached to contacts, accounts, and deals. | Datasources is specific to Apache Druid and Notes to Freshworks CRM — each maps to any object or custom field on the other side. | |
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Contacts Unified sales-and-marketing person records; the core entity for bidirectional syncs. | Segments is specific to Apache Druid and Contacts to Freshworks CRM — 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. | Accounts Company records kept consistent with ERP and billing systems. | Dimensions is specific to Apache Druid and Accounts to Freshworks CRM — each maps to any object or custom field on the other side. |
How changes propagate between Apache Druid and Freshworks CRM
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 Freshworks CRM 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 Freshworks CRM through its API, with automatic retries and rate-limit backoff.
Freshworks CRM Apache Druid Sub-second propagation
DetectionFreshworks CRM notifies Stacksync of record changes through webhook events. Polling with updated-at filters.
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.
- Freshworks CRM: Subject to per-account API rate limits that vary by plan.
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.
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.
- 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.
Apache Druid
connected
Freshworks CRM
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 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.
objects · Apache Druid ⇄ Freshworks CRMCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Apache Druid Freshworks CRMCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Druid and Freshworks CRM integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Druid and Freshworks CRM: authenticate both systems, choose the objects to sync (such as Apache Druid's Tasks and Ingestion Supervisors), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Apache Druid and Freshworks CRM records are not retained after a sync operation.
-
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Druid and Freshworks CRM connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Druid–Freshworks CRM integration in-house.
-
Yes — Stacksync ships production-grade connectors for both Apache Druid and Freshworks CRM. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
-
Change detection on Apache Druid: Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates. On Freshworks CRM: Polling with updated-at filters; outbound webhooks can be configured through workflow automations. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
-
On the Freshworks CRM side: Lists, Notes, Contacts, Accounts, plus custom fields where Freshworks CRM exposes them. On the Apache Druid side: Tasks, Datasources, Segments, Dimensions. Stacksync auto-detects both schemas and converts types between the two systems.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 456 integrations available for Apache Druid and Freshworks CRM.
Popular · 8 of 456
-
Apache Druid ⇄ Zoho CRM -
Apache Druid ⇄ Shopify -
Apache Druid ⇄ Affinity -
Apache Druid ⇄ 6sense -
Freshworks CRM ⇄ Apache Impala -
Freshworks CRM ⇄ Apache Kylin -
Freshworks CRM ⇄ Apache Pinot -
Freshworks CRM ⇄ Azure Synapse Analytics
No integrations match .
