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Database ⇄ CRM

Amazon Aurora to DealCloud integration — real-time, two-way sync

Keep Amazon Aurora 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 Amazon Aurora and DealCloud

Treat DealCloud like part of your database: its records live in Amazon Aurora as real tables, and writes in either place sync to the other in seconds.

Product and engineering teams constantly need CRM data, and the CRM API is a poor way to get it: rate limits, pagination, custom objects, and integration code that breaks when an admin renames a field. What they actually want is the data in Amazon Aurora, where it can be queried and joined like everything else.

Stacksync mirrors Relationship, Activity, Task, User from DealCloud into Views, Materialized Views, Columns and Data Types, Primary and Foreign Keys in Amazon Aurora with real-time, bi-directional sync. Read CRM records with plain queries; write updates from your application and they appear in DealCloud with validation intact. Go-to-market teams keep working in the CRM, engineers keep working in the database, and neither has to think about the other.

Common use cases

  • 01 Keep DealCloud contacts and relationships in sync with an enrichment or email platform to maintain accurate firm-wide relationship intelligence.
  • 02 Mirror DealCloud activities and tasks into an operational database to power internal dashboards without hitting the API on every read.
  • 03 Stream row-level changes from Aurora into a warehouse for near-real-time analytics without batch exports.
  • 04 Consolidate several Aurora clusters into one reporting database.

Common sync patterns

Product events onto CRM records

Signup, usage, or lifecycle changes written to Amazon Aurora sync onto the matching records in DealCloud, giving go-to-market teams live product context.

Internal tools without API code

Back-office apps read and write the synced tables; Stacksync handles the DealCloud API, limits, and retries.

Trigger workflows from CRM changes

Field and stage updates in DealCloud arrive as row changes in Amazon Aurora, ready to drive jobs and notifications.

What you can sync between Amazon Aurora 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.

Amazon Aurora objects DealCloud objects How this pairing syncs
Tables Relational tables synced bi-directionally at row level. Investment Synced with incremental and full sync. Tables is specific to Amazon Aurora and Investment to DealCloud — each maps to any object or custom field on the other side.
Views Read-only query-backed sources for downstream syncs. Relationship Synced with incremental and full sync. Views is specific to Amazon Aurora and Relationship to DealCloud — each maps to any object or custom field on the other side.
Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. Activity Synced with incremental and full sync. Materialized Views is specific to Amazon Aurora and Activity to DealCloud — each maps to any object or custom field on the other side.
Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. Task Synced with incremental and full sync. Columns and Data Types is specific to Amazon Aurora and Task to DealCloud — each maps to any object or custom field on the other side.
Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. User Synced with incremental and full sync. Primary and Foreign Keys is specific to Amazon Aurora and User to DealCloud — each maps to any object or custom field on the other side.
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Deal Synced with incremental and full sync. Read Replicas is specific to Amazon Aurora and Deal to DealCloud — each maps to any object or custom field on the other side.

How changes propagate between Amazon Aurora 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.

Amazon Aurora DealCloud Sub-second propagation

DetectionChanges in Amazon Aurora are captured at the source via change data capture — no polling loop against its API. Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters.

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

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

Rate-limit considerations

  • Amazon Aurora: No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits.
  • DealCloud: API request limits apply per firm tenant; Stacksync manages throttling and retries automatically.
What ships with Amazon Aurora ⇄ DealCloud

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora 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 Amazon Aurora or DealCloud record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Amazon Aurora and DealCloud connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection 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 Amazon Aurora 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 Amazon Aurora 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
    Amazon Aurora connected
    DealCloud connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Amazon Aurora 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 Amazon Aurora and DealCloud.

Popular · 8 of 392
All Amazon Aurora integrations All DealCloud integrations Browse all integrations
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