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Data warehouse ⇄ CRM

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

Keep Apache Druid and Freshsales 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Apache Druid and Freshsales

Sync Freshsales 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. Accounts, Deals, Tasks, Appointments from Freshsales land in Apache Druid as live tables, updated within seconds, and columns computed in Apache Druid write back to fields in Freshsales. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Keep Freshsales accounts aligned with a billing system so plan and revenue fields stay current.
  • 02 Sync sales activities and appointments into analytics tools for rep productivity reporting.
  • 03 Expose product telemetry stored in Druid to business tools without granting direct cluster access.
  • 04 Query aggregated event metrics from Druid and sync them into CRM account fields for usage-based selling.

Common sync patterns

A single customer view

Join Freshsales'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.

CRM analytics on live data

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

What you can sync between Apache Druid and Freshsales

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 Freshsales objects How this pairing syncs
Tasks Batch ingestion and compaction jobs monitored during data loads. Tasks Rep to-dos created from external triggers or synced for productivity reporting. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. Contacts Person records carrying lifecycle stages; the main entity for two-way CRM syncs. Metrics is specific to Apache Druid and Contacts to Freshsales — each maps to any object or custom field on the other side.
Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. Accounts Company records kept aligned with billing, ERP, and warehouse tables. Ingestion Supervisors is specific to Apache Druid and Accounts to Freshsales — each maps to any object or custom field on the other side.
Lookups Key-value mappings joined at query time, refreshable from external systems. Deals Pipeline records synced to a warehouse for revenue reporting. Lookups is specific to Apache Druid and Deals to Freshsales — 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. Appointments Scheduled meetings readable for activity analytics. Datasources is specific to Apache Druid and Appointments to Freshsales — each maps to any object or custom field on the other side.
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Notes Free-text records attached to contacts, accounts, and deals. Segments is specific to Apache Druid and Notes to Freshsales — each maps to any object or custom field on the other side.

How changes propagate between Apache Druid and Freshsales

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 Freshsales 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 Freshsales through its API, with automatic retries and rate-limit backoff.

Freshsales Apache Druid Sub-second propagation

DetectionFreshsales notifies Stacksync of record changes through webhook events. Polling with updated-at filters through views.

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.
  • Freshsales: Subject to per-account API rate limits that vary by Freshworks plan.
What ships with Apache Druid ⇄ Freshsales

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or Freshsales 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 Freshsales record.

Observability

Monitoring

Track your Apache Druid ⇄ Freshsales 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 Freshsales.

How the Apache Druid and Freshsales 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

Freshsales

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

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

    Choose tables

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

Apache Druid and Freshsales 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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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 456 integrations available for Apache Druid and Freshsales.

Popular · 7 of 456
All Apache Druid integrations All Freshsales integrations Browse all integrations
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