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Database ⇄ Data warehouse

Dynamo DB to Snowflake integration — real-time, two-way sync

Keep Dynamo DB and Snowflake 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 Dynamo DB and Snowflake

Connect Dynamo DB and Snowflake with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Dynamo DB's rows in Snowflake, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Dynamo DB where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in Dynamo DB sync into Snowflake in real time, and result tables in Snowflake sync back into Dynamo DB, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Feed finance reconciliation models from ERP data landed in Snowflake on a continuous basis
  • 02 Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
  • 03 Replace ad-hoc export scripts with continuous sync driven by DynamoDB Streams.
  • 04 Stream item changes from DynamoDB application tables into a Postgres or warehouse copy for SQL analytics.

Common sync patterns

Serve warehouse results at database speed

Aggregates or model outputs computed in Snowflake sync into Dynamo DB, where whatever reads from that database gets them without querying the warehouse.

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

Offload heavy reads

Point analytical queries at the synced copy in Snowflake and keep Dynamo DB focused on its operational workload.

What you can sync between Dynamo DB and Snowflake

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.

Dynamo DB objects Snowflake objects How this pairing syncs
Tables The top-level containers a sync targets; each table is addressed independently. Tables The main landing and activation target for synced records. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Items Schemaless records keyed by partition (and optional sort) key, mapped to rows or SaaS objects in syncs. Tasks Scheduled SQL used to transform synced data after it lands. Items is specific to Dynamo DB and Tasks to Snowflake — each maps to any object or custom field on the other side.
Attributes Per-item fields, including nested maps and lists, flattened or mapped during sync. VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. Attributes is specific to Dynamo DB and VARIANT Columns to Snowflake — each maps to any object or custom field on the other side.
Partition and Sort Keys The primary key pair used as the match key for bi-directional sync. Virtual Warehouses The compute a sync's queries run on, sized independently of storage. Partition and Sort Keys is specific to Dynamo DB and Virtual Warehouses to Snowflake — each maps to any object or custom field on the other side.
Global Secondary Indexes Alternate access paths used when sync queries filter on non-key attributes. Databases Top-level containers that scope which data a sync can touch. Global Secondary Indexes is specific to Dynamo DB and Databases to Snowflake — each maps to any object or custom field on the other side.
DynamoDB Streams Ordered item-level change records consumed for incremental sync. Schemas Namespaces within a database used to organize synced tables. DynamoDB Streams is specific to Dynamo DB and Schemas to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between Dynamo DB and Snowflake

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.

Dynamo DB Snowflake Sub-second propagation

DetectionChanges in Dynamo DB are captured at the source via change data capture — no polling loop against its API. Item-level change streams via DynamoDB Streams or Kinesis Data Streams integration.

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

Snowflake Dynamo DB Sub-second propagation

DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.

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

Rate-limit considerations

  • Dynamo DB: Throughput is governed by the table's provisioned or on-demand capacity mode.
  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Dynamo DB ⇄ Snowflake

Connect Dynamo DB and Snowflake for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Dynamo DB–Snowflake connection.

Real-time

Two-way sync

Changes in Dynamo DB or Snowflake instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Dynamo DB or Snowflake 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 Dynamo DB or Snowflake record.

Observability

Monitoring

Track your Dynamo DB ⇄ Snowflake sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Dynamo DB and Snowflake.

How the Dynamo DB and Snowflake connectors work

Dynamo DB

Integration surface
Proprietary JSON-over-HTTPS API accessed through AWS SDKs; PartiQL supported for SQL-like queries
Authentication
AWS IAM credentials with SigV4 request signing
Change detection
Item-level change streams via DynamoDB Streams or Kinesis Data Streams integration
Capabilities
read · write · CDC
Rate limits
Throughput is governed by the table's provisioned or on-demand capacity mode

Snowflake

Integration surface
SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
Authentication
Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
Change detection
Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
Capabilities
read · write · CDC
Rate limits
No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
Snowflake setup guide
How it works

How to connect Dynamo DB to Snowflake — 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 Dynamo DB and Snowflake 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
    Dynamo DB connected
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Dynamo DB and Snowflake 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 · Dynamo DB ⇄ Snowflake
    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
    Dynamo DB Snowflake
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Dynamo DB and Snowflake 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
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ISO 27001
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GDPR
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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 491 integrations available for Dynamo DB and Snowflake.

Popular · 5 of 491
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