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Storage ⇄ Data warehouse

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

Keep Amazon 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.

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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 Dremio

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

Dremio 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 Dremio, or a result in Dremio 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 Jobs, Sources, Physical datasets, Virtual datasets (views) in Dremio 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 Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.
  • 02 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
  • 03 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.
  • 04 Two-way sync Object tags with a database so retention or classification labels set in an internal app write back onto S3 objects via the tagging API without rewriting the files.

Common sync patterns

One dataset, kept consistent both ways

Where the same dataset lives as a file in Amazon S3 and a table in Dremio, 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 Jobs, Sources, Physical datasets, Virtual datasets (views) in Dremio, so file metadata can be joined against the rest of your data and reported on.

Where Dremio computes the labels: push them onto the files

Classifications, scores, or status derived in Dremio 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 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.

Amazon S3 objects Dremio objects How this pairing syncs
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. Reflections Materialized accelerations that make repeated extraction queries cheaper. Object versions is specific to Amazon S3 and Reflections to Dremio — 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. Jobs Query execution records useful for monitoring sync workloads. Prefixes (folders) is specific to Amazon S3 and Jobs to Dremio — 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. Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. Multipart uploads is specific to Amazon S3 and Sources to Dremio — 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. Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. Buckets is specific to Amazon S3 and Physical datasets to Dremio — 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. Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. Objects is specific to Amazon S3 and Virtual datasets (views) to Dremio — 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. Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. Object metadata is specific to Amazon S3 and Apache Iceberg tables to Dremio — each maps to any object or custom field on the other side.

How changes propagate between Amazon S3 and Dremio

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

Dremio Amazon S3 Interval-based propagation

DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.

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.
  • Dremio: Bounded by engine capacity and workload management rather than API rate limits.
What ships with Amazon S3 ⇄ Dremio

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon 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 Amazon S3 or Dremio record.

Observability

Monitoring

Track your Amazon 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 Amazon S3 and Dremio.

How the Amazon S3 and Dremio 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.

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 Amazon 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 Amazon 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
    Amazon S3 connected
    Dremio connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Amazon S3 and Dremio 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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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 425 integrations available for Amazon S3 and Dremio.

Popular · 7 of 425
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