Real-time
Two-way sync
Changes in Dremio or Elasticsearch instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Dremio to Elasticsearch integration — real-time, two-way sync
Keep Dremio and Elasticsearch 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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Migrated from MuleSoft
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Migrated from Matillion
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Dremio and Elasticsearch
Connect Elasticsearch and Dremio with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.
Operational databases and analytical warehouses want the same data at different moments. Analysts want Elasticsearch's rows in Dremio, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in Elasticsearch where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Elasticsearch sync into Dremio in real time, and result tables in Dremio sync back into Elasticsearch, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
- 02 Consolidate data from multiple lake sources through one Dremio semantic layer into a single warehouse target.
- 03 Feed enriched customer records into an index used for vector or hybrid search in AI applications.
- 04 Sync CRM accounts and contacts into an Elasticsearch index to power internal search across customer records.
Common sync patterns
Operational data in the warehouse, minus the pipeline
Rows from Elasticsearch land in Dremio as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Dremio sync into Elasticsearch, where whatever reads from that database gets them without querying the warehouse.
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
What you can sync between Dremio and Elasticsearch
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.
| Dremio objects | Elasticsearch objects | How this pairing syncs | |
|---|---|---|---|
| Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. | Indices Target containers for synced records; each holds a table-like collection of JSON documents. | Sources is specific to Dremio and Indices to Elasticsearch — each maps to any object or custom field on the other side. | |
| Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. | Documents The unit of sync; JSON records created, updated, and deleted by _id. | Physical datasets is specific to Dremio and Documents to Elasticsearch — each maps to any object or custom field on the other side. | |
| Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. | Index mappings Field type definitions that determine how synced fields are indexed and queried. | Virtual datasets (views) is specific to Dremio and Index mappings to Elasticsearch — each maps to any object or custom field on the other side. | |
| Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. | Aliases Stable read/write names that let a sync cut over between index versions without downtime. | Apache Iceberg tables is specific to Dremio and Aliases to Elasticsearch — each maps to any object or custom field on the other side. | |
| Spaces and folders Namespaces that organize virtual datasets and govern access. | Data streams Append-only targets for time-series or event data pushed from source systems. | Spaces and folders is specific to Dremio and Data streams to Elasticsearch — each maps to any object or custom field on the other side. | |
| Reflections Materialized accelerations that make repeated extraction queries cheaper. | Ingest pipelines Server-side transforms applied to documents as a sync writes them. | Reflections is specific to Dremio and Ingest pipelines to Elasticsearch — each maps to any object or custom field on the other side. |
How changes propagate between Dremio and Elasticsearch
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.
Dremio Elasticsearch 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 Elasticsearch through its API, with automatic retries and rate-limit backoff.
Elasticsearch Dremio Interval-based propagation
DetectionStacksync polls Elasticsearch for changes on an incremental schedule, reading only records changed since the previous pass. Polling on timestamp or sequence fields.
DeliveryEach detected change is applied to Dremio as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Dremio: Bounded by engine capacity and workload management rather than API rate limits.
- Elasticsearch: No fixed request quota; throughput is bounded by cluster sizing, thread pools, and bulk queue capacity.
What ships with Dremio ⇄ Elasticsearch
Connect Dremio and Elasticsearch for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Dremio–Elasticsearch connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Dremio or Elasticsearch 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 Dremio or Elasticsearch record.
Observability
Monitoring
Track your Dremio ⇄ Elasticsearch sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Dremio and Elasticsearch.
How the Dremio and Elasticsearch connectors work
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
Elasticsearch
- Integration surface
- REST API (JSON over HTTP)
- Authentication
- API keys or basic authentication; Elastic Cloud also issues service account tokens
- Change detection
- Polling on timestamp or sequence fields; Elasticsearch does not expose a native change feed or webhooks
- Capabilities
- read · write
- Rate limits
- No fixed request quota; throughput is bounded by cluster sizing, thread pools, and bulk queue capacity
How it works
How to connect Dremio to Elasticsearch — 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 Dremio and Elasticsearch with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Dremio
connected
Elasticsearch
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Dremio and Elasticsearch 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 · Dremio ⇄ ElasticsearchCustomers 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.
Dremio ElasticsearchCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Dremio and Elasticsearch integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Dremio and Elasticsearch: authenticate both systems, choose the objects to sync (such as Dremio's Sources and Physical datasets), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Dremio and Elasticsearch records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Dremio and Elasticsearch connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Dremio–Elasticsearch integration in-house.
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Yes — Stacksync ships production-grade connectors for both Dremio and Elasticsearch. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. On Elasticsearch: Polling on timestamp or sequence fields; Elasticsearch does not expose a native change feed or webhooks. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Dremio side: Jobs, Sources, Physical datasets, Virtual datasets (views), plus custom fields where Dremio exposes them. On the Elasticsearch side: Documents, Index mappings, Aliases, Data streams. Stacksync auto-detects both schemas and converts types between the two systems.
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 448 integrations available for Dremio and Elasticsearch.
Popular · 4 of 448
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Dremio ⇄ Amazon DynamoDB -
Elasticsearch ⇄ Databricks -
Elasticsearch ⇄ AWS S3 -
Elasticsearch ⇄ Amazon Redshift
No integrations match .
