Real-time
Two-way sync
Changes in Amazon S3 or ClickHouse instantly reflect in both systems. No stale data, no manual imports.
Storage ⇄ Data warehouse
Amazon S3 to ClickHouse integration — real-time, two-way sync
Keep Amazon S3 and ClickHouse 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 ClickHouse
Bridge query-ready tables and stored files: ClickHouse and Amazon S3 keep the same records in step, in real time, in both directions.
ClickHouse 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 ClickHouse, or a result in ClickHouse 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, Distributed tables, Dictionaries, Tables (MergeTree family) in ClickHouse with Object metadata, Object tags, Object versions, Prefixes (folders) 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 Sync aggregated ClickHouse query results back into operational tools, such as account-level usage metrics into a CRM.
- 02 Consolidate logs and business records from multiple sources into MergeTree tables for retention and reporting.
- 03 Mirror Objects under a given Bucket and prefix into another bucket, region, or a warehouse external stage for backup or downstream processing.
- 04 Trigger a database or ERP record update the moment an S3 Event Notification fires an object-created event, for example when a partner drops an EDI or invoice file into an inbound prefix.
Common sync patterns
Where ClickHouse computes the labels: push them onto the files
Classifications, scores, or status derived in ClickHouse are written back onto the matching Object metadata, Object tags, Object versions, Prefixes (folders) in Amazon S3 as metadata or tags, so the file store reflects what analytics decided.
Where Amazon S3 receives the raw files: land them as query-ready rows
Files and exports that arrive in Amazon S3 are parsed into Materialized views, Distributed tables, Dictionaries, Tables (MergeTree family) in ClickHouse as they land, so analysts query current data instead of waiting on the next scheduled load.
Where Amazon S3 is the shared drive: publish results back as files
Curated tables and query results from ClickHouse are written to Amazon S3 as files the rest of the business can open, keeping the shared copy current without a hand-run export.
What you can sync between Amazon S3 and ClickHouse
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 | ClickHouse 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. | Databases Namespaces that group tables and scope permissions for sync users. | Buckets is specific to Amazon S3 and Databases to ClickHouse — 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. | Views Saved queries used as curated, read-only sync sources. | Objects is specific to Amazon S3 and Views to ClickHouse — 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. | Materialized views Insert-time transformations that reshape incoming synced rows into aggregates. | Object metadata is specific to Amazon S3 and Materialized views to ClickHouse — 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. | Distributed tables Query-routing tables over cluster shards in self-managed deployments. | Object tags is specific to Amazon S3 and Distributed tables to ClickHouse — 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. | Dictionaries In-memory lookup structures refreshed from external sources, sometimes fed by syncs. | Object versions is specific to Amazon S3 and Dictionaries to ClickHouse — 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. | Tables (MergeTree family) Columnar, append-optimized tables that serve as the destination for high-volume sync loads. | Prefixes (folders) is specific to Amazon S3 and Tables (MergeTree family) to ClickHouse — each maps to any object or custom field on the other side. |
How changes propagate between Amazon S3 and ClickHouse
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 ClickHouse 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 ClickHouse as a row-level write, with types converted between the two schemas.
ClickHouse Amazon S3 Interval-based propagation
DetectionStacksync polls ClickHouse for changes on an incremental schedule, reading only records changed since the previous pass. No log-based CDC for consumers.
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 ⇄ ClickHouse
Connect Amazon S3 and ClickHouse for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–ClickHouse connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Amazon S3 or ClickHouse 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 ClickHouse record.
Observability
Monitoring
Track your Amazon S3 ⇄ ClickHouse 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 ClickHouse.
How the Amazon S3 and ClickHouse 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.
ClickHouse
- Integration surface
- Native TCP protocol and HTTP interface; standard SQL dialect, with MySQL and PostgreSQL wire compatibility available
- Authentication
- Database credentials (username/password); ClickHouse Cloud issues per-service credentials over TLS
- Change detection
- No log-based CDC for consumers; incremental reads use polling on monotonic columns, and ClickHouse is usually the destination rather than the source
- Capabilities
- read · write
How it works
How to connect Amazon S3 to ClickHouse — 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.
- 01
Connect your apps
Authenticate Amazon S3 and ClickHouse with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Amazon S3
connected
ClickHouse
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Amazon S3 and ClickHouse 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.
objects · Amazon S3 ⇄ ClickHouseCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Amazon S3 ClickHouseCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Amazon S3 and ClickHouse integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Amazon S3 and ClickHouse: authenticate both systems, choose the objects to sync (such as Amazon S3's Buckets and Objects), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the ClickHouse side: Materialized views, Distributed tables, Dictionaries, Tables (MergeTree family), plus custom fields where ClickHouse exposes them. On the Amazon S3 side: Object metadata, Object tags, Object versions, Prefixes (folders). Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Amazon S3 and ClickHouse: Where ClickHouse computes the labels: push them onto the files; Where Amazon S3 receives the raw files: land them as query-ready rows; Where Amazon S3 is the shared drive: publish results back as files. Classifications, scores, or status derived in ClickHouse are written back onto the matching Object metadata, Object tags, Object versions, Prefixes (folders) in Amazon S3 as metadata or tags, so the file store reflects what analytics decided.
-
Amazon S3: 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. ClickHouse: Native TCP protocol and HTTP interface; standard SQL dialect, with MySQL and PostgreSQL wire compatibility available. Authentication: Database credentials (username/password); ClickHouse Cloud issues per-service credentials over TLS. Stacksync manages authentication, retries, and rate limits on both sides.
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ClickHouse: Updates and deletes are asynchronous mutations that rewrite data parts, so syncs into ClickHouse favor append-and-deduplicate patterns (for example ReplacingMergeTree) over row-level upserts. Amazon S3: Objects can be up to 5 TB, but a single PUT is capped at 5 GB, so larger files must use multipart upload, and objects in Glacier storage classes must be restored before their bytes can be read. Stacksync's field mapping accounts for these differences between Amazon S3 and ClickHouse without custom code.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 531 integrations available for Amazon S3 and ClickHouse.
Popular · 7 of 531
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Amazon S3 ⇄ Teradata Vantage -
Amazon S3 ⇄ Tinybird -
Amazon S3 ⇄ Vertica -
Amazon S3 ⇄ Yellowbrick -
ClickHouse ⇄ Egnyte -
ClickHouse ⇄ Box -
ClickHouse ⇄ Sharepoint
No integrations match .
