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Data warehouse ⇄ CRM

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

Keep Apache Druid and DealCloud 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 DealCloud

Sync DealCloud 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. Activity, Task, User, Deal from DealCloud land in Apache Druid as live tables, updated within seconds, and columns computed in Apache Druid write back to fields in DealCloud. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Push portfolio and fund performance data from a warehouse into DealCloud so dealmakers see up-to-date metrics on each investment.
  • 02 Keep DealCloud contacts and relationships in sync with an enrichment or email platform to maintain accurate firm-wide relationship intelligence.
  • 03 Feed operational records into Druid via batch ingestion so analysts get interactive slice-and-dice on fresh data.
  • 04 Sync Druid query results into a warehouse to combine real-time aggregates with historical models.

Common sync patterns

A single customer view

Join DealCloud'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 DealCloud 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 DealCloud

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 DealCloud objects How this pairing syncs
Tasks Batch ingestion and compaction jobs monitored during data loads. Task Synced with incremental and full sync. 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. Relationship Synced with incremental and full sync. Metrics is specific to Apache Druid and Relationship to DealCloud — 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. Activity Synced with incremental and full sync. Ingestion Supervisors is specific to Apache Druid and Activity to DealCloud — each maps to any object or custom field on the other side.
Lookups Key-value mappings joined at query time, refreshable from external systems. User Synced with incremental and full sync. Lookups is specific to Apache Druid and User to DealCloud — 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. Deal Synced with incremental and full sync. Datasources is specific to Apache Druid and Deal to DealCloud — each maps to any object or custom field on the other side.
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Company Synced with incremental and full sync. Segments is specific to Apache Druid and Company to DealCloud — each maps to any object or custom field on the other side.

How changes propagate between Apache Druid and DealCloud

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

DealCloud Apache Druid Interval-based propagation

DetectionStacksync polls DealCloud for changes on an incremental schedule, reading only records changed since the previous pass. Incremental via each entry's last-modified timestamp.

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.
  • DealCloud: API request limits apply per firm tenant; Stacksync manages throttling and retries automatically.
What ships with Apache Druid ⇄ DealCloud

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or DealCloud 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 DealCloud record.

Observability

Monitoring

Track your Apache Druid ⇄ DealCloud 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 DealCloud.

How the Apache Druid and DealCloud 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

DealCloud

Integration surface
REST API (DealCloud Data API v2)
Authentication
OAuth 2.0 client-credentials; a DealCloud administrator generates a client ID and secret in the DealCloud admin API settings and grants Stacksync the required scopes
Change detection
Incremental via each entry's last-modified timestamp; DealCloud has no universal native change-data-capture, so Stacksync polls modified rows on an interval
Capabilities
read · write
Rate limits
API request limits apply per firm tenant; Stacksync manages throttling and retries automatically.
How it works

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

    Choose tables

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

Apache Druid and DealCloud 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 392 integrations available for Apache Druid and DealCloud.

Popular · 8 of 392
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