Skip to content
Storage ⇄ Database

Amazon S3 to DuckDB integration — real-time, two-way sync

Keep Amazon S3 and DuckDB 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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Amazon S3 and DuckDB

Keep DuckDB and Amazon S3 in step: a row in DuckDB for every file in Amazon S3, with names, paths, metadata, and status staying consistent in real time, in both directions.

A database holds structured records; a storage system holds the files those records depend on, such as contracts, images, exports, uploads, and documents. The two describe the same things from opposite sides: a row in DuckDB says a file exists and carries its name, location, and status, while Amazon S3 holds the bytes. Linked only by a hand-kept path or a one-off script, the two drift the moment a file is renamed, moved, or deleted and the record still points at where it used to be.

Stacksync syncs External files (Parquet/CSV/JSON), Attached databases, Database files, Schemas in DuckDB with Object tags, Object versions, Prefixes (folders), Multipart uploads in Amazon S3 bi-directionally and in real time. File attributes, including name, path or object key, size, type, modified time, owner, and tags or custom properties, map field by field to columns on the matching row, and a change on either side shows up on the other within seconds. New files appear as rows, metadata edits travel in the direction you choose, and deletes stay consistent, with conflict rules you set in place of nightly reconciliation scripts.

Common use cases

  • 01 Push aggregates computed in DuckDB out to a CRM or business tools so analysis results reach operational systems.
  • 02 Use DuckDB as a transform step: read synced Parquet exports, aggregate with SQL, and write results back to an operational database.
  • 03 Reconcile Object versions and delete markers into an audit table so teams track when files under a compliance prefix were added, replaced, or removed.
  • 04 Index every new Object's key, size, LastModified, and user metadata into a Postgres catalog table so applications query S3 contents in SQL instead of paging ListObjectsV2.

Common sync patterns

A landing zone for incoming files

Files that arrive in a folder or bucket in Amazon S3 become rows in DuckDB as they land, so a database-driven process can pick them up without polling the storage system's API.

Continuous archival to file storage

Rows from DuckDB are written out to Amazon S3 as files on a schedule or as they change, giving a durable, low-cost copy for backup, compliance, or a data lake, without a custom export job to maintain.

A queryable index of the file store

Every file or object in Amazon S3 shows up as a row in DuckDB, with its name, folder or key, size, type, and modified date as columns, so the contents of the store can be listed, filtered, and joined like any other table.

What you can sync between Amazon S3 and DuckDB

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 S3 objects DuckDB objects How this pairing syncs
Buckets Top-level, region-scoped containers that hold objects; enumerated to discover the namespaces and prefixes a sync should cover. Attached databases Additional database files or external systems attached into one session for cross-source queries. Buckets is specific to Amazon S3 and Attached databases to DuckDB — each maps to any object or custom field on the other side.
Objects Files stored under a key; content is read with GET and written with PUT, and each object's key/size/ETag/LastModified is the unit indexed into a database. Database files Single-file .duckdb databases that jobs read and write directly on disk or object storage. Objects is specific to Amazon S3 and Database files to DuckDB — each maps to any object or custom field on the other side.
Object metadata System metadata (Content-Type, size, ETag, LastModified) plus user-defined x-amz-meta-* headers; user metadata is fixed at write time and only changeable by rewriting the object. Schemas Namespaces within a database used to organize tables in sync outputs. Object metadata is specific to Amazon S3 and Schemas to DuckDB — each maps to any object or custom field on the other side.
Object tags Up to 10 key-value tags per object, mutable in place via the tagging API independent of content, so classification and retention labels sync two-way without rewriting files. Tables Columnar tables created via SQL; the destination for materialized sync data. Object tags is specific to Amazon S3 and Tables to DuckDB — each maps to any object or custom field on the other side.
Object versions When bucket versioning is enabled every write creates a new version ID; prior versions and delete markers are readable for history and audit syncs. Views SQL views used to shape or filter data for downstream consumers. Object versions is specific to Amazon S3 and Views to DuckDB — each maps to any object or custom field on the other side.
Prefixes (folders) Logical path segments in object keys used to scope a sync and to parallelize throughput, since S3 rate limits partition by prefix. External files (Parquet/CSV/JSON) Files DuckDB queries in place without loading, common as a sync interchange format. Prefixes (folders) is specific to Amazon S3 and External files (Parquet/CSV/JSON) to DuckDB — each maps to any object or custom field on the other side.

How changes propagate between Amazon S3 and DuckDB

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 S3 DuckDB Sub-second propagation

DetectionAmazon S3 notifies Stacksync of record changes through webhook events. S3 Event Notifications push object-created, object-removed, and object-tagging events to SNS, SQS, Lambda, or EventBridge.

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

DuckDB Amazon S3 Interval-based propagation

DetectionStacksync polls DuckDB for changes on an incremental schedule, reading only records changed since the previous pass. Polling or full re-reads.

DeliveryEach detected change is written to Amazon S3 through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Amazon S3: S3 sustains at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second per partitioned prefix and scales higher automatically; bursts can return HTTP 503 SlowDown while it repartitions.
  • DuckDB: No API rate limits; throughput is bounded by local compute and I/O.
What ships with Amazon S3 ⇄ DuckDB

Connect Amazon S3 and DuckDB for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–DuckDB connection.

Real-time

Two-way sync

Changes in Amazon S3 or DuckDB instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon S3 or DuckDB 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 S3 or DuckDB record.

Observability

Monitoring

Track your Amazon S3 ⇄ DuckDB sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon S3 and DuckDB.

How the Amazon S3 and DuckDB connectors work

Amazon S3

Integration surface
S3 REST API (also via AWS SDKs and the S3-compatible endpoint)
Authentication
AWS IAM credentials — an access key ID and secret access key signed with AWS Signature Version 4; supports temporary STS credentials and cross-account IAM roles
Change detection
S3 Event Notifications push object-created, object-removed, and object-tagging events to SNS, SQS, Lambda, or EventBridge; there is no modified-since query, so polling relies on each object's LastModified from ListObjectsV2
Capabilities
read · write · webhooks
Rate limits
S3 sustains at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second per partitioned prefix and scales higher automatically; bursts can return HTTP 503 SlowDown while it repartitions.

DuckDB

Integration surface
In-process SQL engine via client libraries (Python, Node.js, JDBC, CLI); no server or network API by default
Authentication
None built in; access control is file-system level (MotherDuck adds token auth for its hosted service)
Change detection
Polling or full re-reads; no change feed or transaction log API
Capabilities
read · write
Rate limits
No API rate limits; throughput is bounded by local compute and I/O
How it works

How to connect Amazon S3 to DuckDB — 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 S3 and DuckDB 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 S3 connected
    DuckDB connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon S3 and DuckDB 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 S3 ⇄ DuckDB
    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 S3 DuckDB
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Amazon S3 and DuckDB 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
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 439 integrations available for Amazon S3 and DuckDB.

Popular · 7 of 439
All Amazon S3 integrations All DuckDB integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own