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Storage ⇄ Data warehouse

Amazon S3 to Azure Synapse Analytics integration — real-time, two-way sync

Keep Amazon S3 and Azure Synapse Analytics 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 Azure Synapse Analytics

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

Azure Synapse Analytics 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 Azure Synapse Analytics, or a result in Azure Synapse Analytics 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 External tables, Views, Schemas, Materialized views in Azure Synapse Analytics with Object tags, Object versions, Prefixes (folders), Multipart uploads 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 Consolidate SaaS data alongside lake data so analysts join both through one SQL surface.
  • 02 Load CRM and ERP records into Synapse dedicated SQL pool tables for enterprise reporting.
  • 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

One dataset, kept consistent both ways

Where the same dataset lives as a file in Amazon S3 and a table in Azure Synapse Analytics, 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 External tables, Views, Schemas, Materialized views in Azure Synapse Analytics, so file metadata can be joined against the rest of your data and reported on.

Where Azure Synapse Analytics computes the labels: push them onto the files

Classifications, scores, or status derived in Azure Synapse Analytics are written back onto the matching Object tags, Object versions, Prefixes (folders), Multipart uploads in Amazon S3 as metadata or tags, so the file store reflects what analytics decided.

What you can sync between Amazon S3 and Azure Synapse Analytics

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 Azure Synapse Analytics objects How this pairing syncs
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. Views Curated projections used when downstream tools should not read base tables directly. Object metadata is specific to Amazon S3 and Views to Azure Synapse Analytics — 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. Schemas Namespaces that separate staging, integration, and presentation layers. Object tags is specific to Amazon S3 and Schemas to Azure Synapse Analytics — 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. Materialized views Precomputed aggregates that speed reads of frequently synced result sets. Object versions is specific to Amazon S3 and Materialized views to Azure Synapse Analytics — 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. SQL pools Dedicated or serverless compute contexts that determine how and where queries run. Prefixes (folders) is specific to Amazon S3 and SQL pools to Azure Synapse Analytics — 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. Tables (dedicated SQL pool) Distributed warehouse tables that serve as sync destinations for analytics workloads. Multipart uploads is specific to Amazon S3 and Tables (dedicated SQL pool) to Azure Synapse Analytics — each maps to any object or custom field on the other side.
Buckets Top-level, region-scoped containers that hold objects; enumerated to discover the namespaces and prefixes a sync should cover. External tables Tables over files in the data lake, queried through serverless SQL and often read-only in syncs. Buckets is specific to Amazon S3 and External tables to Azure Synapse Analytics — each maps to any object or custom field on the other side.

How changes propagate between Amazon S3 and Azure Synapse Analytics

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 Azure Synapse Analytics 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 Azure Synapse Analytics as a row-level write, with types converted between the two schemas.

Azure Synapse Analytics Amazon S3 Interval-based propagation

DetectionStacksync polls Azure Synapse Analytics for changes on an incremental schedule, reading only records changed since the previous pass. Polling on watermark columns.

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 ⇄ Azure Synapse Analytics

Connect Amazon S3 and Azure Synapse Analytics for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon S3 or Azure Synapse Analytics 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 Azure Synapse Analytics record.

Observability

Monitoring

Track your Amazon S3 ⇄ Azure Synapse Analytics 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 Azure Synapse Analytics.

How the Amazon S3 and Azure Synapse Analytics 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.

Azure Synapse Analytics

Integration surface
SQL wire protocol (TDS) with T-SQL for SQL pools; additional Spark and pipeline surfaces exist but syncs use the SQL endpoint
Authentication
SQL authentication or Microsoft Entra ID
Change detection
Polling on watermark columns; Synapse SQL pools do not expose log-based CDC for downstream consumers
Capabilities
read · write
How it works

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

    Choose tables

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

Amazon S3 and Azure Synapse Analytics 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 532 integrations available for Amazon S3 and Azure Synapse Analytics.

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