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Data warehouse

AWS S3 to Dremio integration — real-time, two-way sync

Keep AWS S3 and Dremio 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

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Why teams connect AWS S3 and Dremio

Keep tables consistent across AWS S3 and Dremio, for a migration, a multi-warehouse stack, or a dataset two platforms both need.

Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.

Stacksync syncs tables between AWS S3 and Dremio continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.

Common use cases

  • Export synced operational data to S3 as files feeding a data lake or downstream batch jobs.
  • Trigger incremental sync runs from S3 event notifications when new files land in a prefix.
  • Sync curated Dremio views into an operational Postgres so applications get low-latency access to lakehouse data.
  • Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.

Serve tools that only connect to one platform

Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

What you can sync between AWS S3 and Dremio

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.

AWS S3 objects Dremio objects
Prefixes Key-name paths used to partition synced datasets, since S3 has no real directories. Reflections Materialized accelerations that make repeated extraction queries cheaper.
Object Metadata System and user-defined metadata read alongside object contents. Jobs Query execution records useful for monitoring sync workloads.
Object Versions Prior copies retained when versioning is enabled, relevant for reprocessing. Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place.
Event Notifications Notifications on object creation or deletion that trigger incremental processing. Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads.
Access Points Scoped network endpoints used to grant a sync narrow access to a bucket. Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts.
Multipart Uploads The mechanism used to write large export files reliably. Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads.
What ships with AWS S3 ⇄ Dremio

Connect AWS S3 and Dremio for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS S3 or Dremio 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 AWS S3 or Dremio record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS S3 and Dremio.

How the AWS S3 and Dremio connectors work

AWS S3

Integration surface
REST API (the S3 API), accessed directly or through AWS SDKs
Authentication
AWS IAM credentials with SigV4 signing; commonly a role scoped to specific buckets and prefixes
Change detection
S3 Event Notifications on object create/delete delivered to SQS, SNS, Lambda, or EventBridge; list-based polling as a fallback
Capabilities
read · write · webhooks
Rate limits
Request throughput scales per prefix; sustained high-volume workloads should spread keys across prefixes

Dremio

Integration surface
Arrow Flight SQL, JDBC/ODBC, and a REST API
Authentication
Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
Change detection
Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
Capabilities
read · write
Rate limits
Bounded by engine capacity and workload management rather than API rate limits
How it works

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

    Choose tables

    Pick the AWS S3 and Dremio 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 · AWS S3 ⇄ Dremio
    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
    AWS S3 Dremio
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS S3 and Dremio integration FAQ

SECURITY

Security teams love 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
CSA STAR
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 386 integrations available for AWS S3 and Dremio.

Popular · 8 of 386
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