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Storage ⇄ Data warehouse

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

Keep Amazon S3 and Materialize 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 S3 and Materialize

Bridge query-ready tables and stored files: Materialize and Amazon S3 keep the same records in step, in real time, in both directions.

Materialize keeps the tables and query results a business reports on; Amazon S3 keeps the raw files, documents, and objects that the same business produces and shares. The two overlap wherever a dataset lives as both — a file dropped in Amazon S3 that has to become rows in Materialize, or a result in Materialize that people downstream need back as a file in Amazon S3. When that overlap is bridged by manual export and import or an overnight job, one side spends the day working from a stale copy.

Stacksync syncs Materialized Views, Sinks, Indexes, Clusters in Materialize with Object versions, Prefixes (folders), Multipart uploads, Buckets in Amazon S3 field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync keeps every copy consistent and resolves conflicts by rules you set.

Common use cases

  • 01 Bridge streaming sources and non-streaming destinations by materializing joins across both.
  • 02 Sync operational CRM or ERP data into Materialize so real-time views stay current without batch loads.
  • 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

Where Amazon S3 is the shared drive: publish results back as files

Curated tables and query results from Materialize are written to Amazon S3 as files the rest of the business can open, keeping the shared copy current without a hand-run export.

One dataset, kept consistent both ways

Where the same dataset lives as a file in Amazon S3 and a table in Materialize, a change on either side propagates to the other, ending the drift between the file people read and the table people query.

Where Amazon S3 holds the file inventory: make it queryable

The catalog of documents, owners, and folders in Amazon S3 appears as Materialized Views, Sinks, Indexes, Clusters in Materialize, so file metadata can be joined against the rest of your data and reported on.

What you can sync between Amazon S3 and Materialize

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 Materialize objects How this pairing syncs
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. Tables User-managed tables that accept INSERT/UPDATE/DELETE from sync pipelines. Objects is specific to Amazon S3 and Tables to Materialize — 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. Sources Ingestion points (Kafka, Postgres CDC, MySQL CDC, webhook) that feed external data into Materialize. Object metadata is specific to Amazon S3 and Sources to Materialize — 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. Materialized Views Incrementally maintained query results that syncs read as continuously up-to-date datasets. Object tags is specific to Amazon S3 and Materialized Views to Materialize — 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. Sinks Outbound connections that emit view changes to Kafka topics. Object versions is specific to Amazon S3 and Sinks to Materialize — 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. Indexes In-memory arrangements that make view reads fast for serving workloads. Prefixes (folders) is specific to Amazon S3 and Indexes to Materialize — each maps to any object or custom field on the other side.
Multipart uploads In-progress large-object uploads assembled from parts; objects above ~100 MB (required above 5 GB) are written this way, and incomplete uploads persist until completed or aborted. Clusters Compute pools that isolate ingestion, view maintenance, and serving. Multipart uploads is specific to Amazon S3 and Clusters to Materialize — each maps to any object or custom field on the other side.

How changes propagate between Amazon S3 and Materialize

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

Materialize Amazon S3 Sub-second propagation

DetectionChanges in Materialize are captured at the source via change data capture — no polling loop against its API. SUBSCRIBE queries stream row-level changes of any view or table to the client.

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.
What ships with Amazon S3 ⇄ Materialize

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon S3 or Materialize 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 Materialize record.

Observability

Monitoring

Track your Amazon S3 ⇄ Materialize 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 Materialize.

How the Amazon S3 and Materialize 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.

Materialize

Integration surface
PostgreSQL wire protocol (SQL)
Authentication
Database credentials (username/password; app passwords in the managed cloud service)
Change detection
SUBSCRIBE queries stream row-level changes of any view or table to the client
Capabilities
read · write · CDC
How it works

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

    Choose tables

    Pick the Amazon S3 and Materialize 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 ⇄ Materialize
    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 Materialize
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon S3 and Materialize 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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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 428 integrations available for Amazon S3 and Materialize.

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