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Communications ⇄ Data warehouse

Amazon SES to Databricks integration — real-time, two-way sync

Keep Amazon SES and Databricks 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 Amazon SES and Databricks

Land the messages, calls, and events from Amazon SES in Databricks as live tables, and write results back, without building or maintaining a pipeline.

Amazon SES produces a constant stream of activity — messages sent and received, calls placed and answered, meetings held, and the delivery and engagement events attached to them. That record is what the rest of the company wants to analyze, and it usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay to get it out.

Stacksync syncs Messages (SendEmail / SendBulkEmail), Contacts, Contact Lists, Email Templates from Amazon SES into tables in Databricks in real time, handling schema, rate limits, and retries. Because the connection works in both directions, results computed in Databricks — segments, contact updates, suppression flags — can be written back into fields in Amazon SES wherever it exposes them, so analysis lands where outreach actually happens.

Common use cases

  • 01 Two-way sync Contacts and their topic subscription state between the SES contact list and a Postgres database or CRM so opt-outs propagate in both directions.
  • 02 Push warehouse or CRM audience segments into SES as Contacts using CreateContact or a bulk CreateImportJob (up to 1M contacts per job) to feed marketing sends.
  • 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

Activity joined with everything else

Combine Amazon SES's activity with the CRM, product, and support data already in Databricks to attribute outcomes to the touches that drove them, which no single tool can do alone.

Where Amazon SES accepts updates: operational write-back

Segments, contact fields, or suppression flags computed in Databricks sync back onto records in Amazon SES, putting warehouse analysis where the outreach happens.

Queryable history that outlives retention

A continuously synced copy in Databricks preserves messages, call logs, and events for reporting and audit even as they age out of Amazon SES or get purged inside it.

What you can sync between Amazon SES and Databricks

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 SES objects Databricks objects How this pairing syncs
Contact Lists One list per AWS account, subdivided into Topics that group subscriptions; read and write via CreateContactList and GetContactList. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Contact Lists is specific to Amazon SES and Schemas to Databricks — each maps to any object or custom field on the other side.
Email Templates Reusable subject/HTML/text templates with replacement variables; synced via CreateEmailTemplate, GetEmailTemplate, ListEmailTemplates, and UpdateEmailTemplate. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Email Templates is specific to Amazon SES and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Suppression List Addresses auto-added on bounce or complaint; read with ListSuppressedDestinations and written with Put/DeleteSuppressedDestination to control re-sends. Views Curated read-only projections used as sync sources for downstream tools. Suppression List is specific to Amazon SES and Views to Databricks — each maps to any object or custom field on the other side.
Verified Identities Email-address and domain identities cleared to send; managed via CreateEmailIdentity, ListEmailIdentities, GetEmailIdentity (DKIM and verification status). Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Verified Identities is specific to Amazon SES and Materialized Views to Databricks — each maps to any object or custom field on the other side.
Configuration Sets Named send-time rule groups that attach event destinations, IP pools, and suppression options; managed via Create/List ConfigurationSet operations. Volumes Unity Catalog file storage used for staging bulk loads. Configuration Sets is specific to Amazon SES and Volumes to Databricks — each maps to any object or custom field on the other side.
Sending Events Per-message send, delivery, bounce, complaint, open, and click events; not on any API, read from a configuration-set event destination (SNS/EventBridge/Firehose). SQL Warehouses The compute endpoint a sync connects to for query execution. Sending Events is specific to Amazon SES and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Amazon SES and Databricks

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 SES Databricks Sub-second propagation

DetectionAmazon SES notifies Stacksync of record changes through webhook events. Message events (delivery, bounce, complaint, open, click) are pushed in near real time through configuration-set event destinations.

DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.

Databricks Amazon SES 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 Amazon SES through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Amazon SES: Every SES API action except SendEmail, SendRawEmail, and SendTemplatedEmail is throttled to 1 request/second per Region; the send rate and 24-hour sending quota are account-specific, adjustable, and per Region.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Amazon SES ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon SES or Databricks 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 SES or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Amazon SES and Databricks connectors work

Amazon SES

Integration surface
AWS SES API v2 (REST/JSON over HTTPS) plus an SMTP sending interface
Authentication
AWS IAM credentials (access key + secret) signed with Signature Version 4, or an assumed IAM role via STS; the SMTP interface uses SMTP credentials derived from an IAM user
Change detection
Message events (delivery, bounce, complaint, open, click) are pushed in near real time through configuration-set event destinations (SNS/EventBridge/Firehose); contacts, templates, and suppression entries are polled via List/Get operations
Capabilities
read · write · webhooks
Rate limits
Every SES API action except SendEmail, SendRawEmail, and SendTemplatedEmail is throttled to 1 request/second per Region; the send rate and 24-hour sending quota are account-specific, adjustable, and per Region

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
How it works

How to connect Amazon SES to Databricks — 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 SES and Databricks 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 SES connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon SES and Databricks 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 SES ⇄ Databricks
    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 SES Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon SES and Databricks 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 454 integrations available for Amazon SES and Databricks.

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