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Data warehouse ⇄ CRM

Databricks to DealCloud integration — real-time, two-way sync

Keep Databricks 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.

  • 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 Databricks and DealCloud

Sync DealCloud into Databricks 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. User, Deal, Company, Contact from DealCloud land in Databricks as live tables, updated within seconds, and columns computed in Databricks 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 Mirror DealCloud activities and tasks into an operational database to power internal dashboards without hitting the API on every read.
  • 02 Push portfolio and fund performance data from a warehouse into DealCloud so dealmakers see up-to-date metrics on each investment.
  • 03 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.

Common sync patterns

Scores and segments back on the record

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

A single customer view

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

Cleanup that sticks

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

What you can sync between Databricks 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.

Databricks objects DealCloud objects How this pairing syncs
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Fund Synced with incremental and full sync. Delta Tables is specific to Databricks and Fund to DealCloud — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Investment Synced with incremental and full sync. Views is specific to Databricks and Investment to DealCloud — each maps to any object or custom field on the other side.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Relationship Synced with incremental and full sync. Materialized Views is specific to Databricks and Relationship to DealCloud — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. Activity Synced with incremental and full sync. Volumes is specific to Databricks and Activity to DealCloud — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Task Synced with incremental and full sync. SQL Warehouses is specific to Databricks and Task to DealCloud — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. User Synced with incremental and full sync. Change Data Feed is specific to Databricks and User to DealCloud — each maps to any object or custom field on the other side.

How changes propagate between Databricks 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.

Databricks DealCloud Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

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

DealCloud Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • DealCloud: API request limits apply per firm tenant; Stacksync manages throttling and retries automatically.
What ships with Databricks ⇄ DealCloud

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks 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 Databricks or DealCloud record.

Observability

Monitoring

Track your Databricks ⇄ DealCloud sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and DealCloud.

How the Databricks and DealCloud connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

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 Databricks 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 Databricks 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
    Databricks connected
    DealCloud connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks 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 · Databricks ⇄ 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
    Databricks DealCloud
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks 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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
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 392 integrations available for Databricks and DealCloud.

Popular · 8 of 392
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