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Elasticsearch to Jdbc integration — real-time, two-way sync

Keep Elasticsearch and Jdbc 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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Why teams connect Elasticsearch and Jdbc

Keep Elasticsearch and Jdbc synchronized in real time, across engines, regions, or services, in one or both directions.

Two databases that must agree is one of the oldest problems in engineering: different engines for different workloads, separate services with overlapping reference data, a migration in flight, or regional instances that share a subset of records. Hand-rolled replication across systems means change capture, conflict handling, and type mapping, all built and maintained by your team.

Stacksync syncs tables or collections between Elasticsearch and Jdbc continuously and bi-directionally, translating types between the two engines and resolving conflicts by rules you configure. Rows written on either side appear on the other within seconds.

Common use cases

  • 01 Sync CRM accounts and contacts into an Elasticsearch index to power internal search across customer records.
  • 02 Push product catalog data from an ERP or commerce database into Elasticsearch for storefront search.
  • 03 Write records from a CRM, ERP, or another app back into database tables via SQL INSERT and UPDATE so the database stays current.
  • 04 Connect a niche or legacy RDBMS that has no dedicated Stacksync connector but ships a JDBC driver, using its JDBC URL to sync it two-way.

Common sync patterns

Shared reference data between services

Services that own separate databases stay consistent on the records they share, without a custom replication layer.

Regional or environment copies

Mirror selected tables to another region or environment continuously, filtered to just the rows that should travel.

Cross-engine sync

Keep the same dataset live in both Elasticsearch and Jdbc, so each workload runs on the engine that suits it.

What you can sync between Elasticsearch and Jdbc

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.

Elasticsearch objects Jdbc objects How this pairing syncs
Index mappings Field type definitions that determine how synced fields are indexed and queried. Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. Index mappings is specific to Elasticsearch and Schemas & catalogs to Jdbc — each maps to any object or custom field on the other side.
Aliases Stable read/write names that let a sync cut over between index versions without downtime. Stored procedures & functions Server-side routines callable via JDBC CallableStatement; invoked for custom read or write logic when a table-level mapping is not enough. Aliases is specific to Elasticsearch and Stored procedures & functions to Jdbc — each maps to any object or custom field on the other side.
Data streams Append-only targets for time-series or event data pushed from source systems. Sequences Server-generated identity values; relevant when writing rows into tables whose keys are assigned by the database rather than the source system. Data streams is specific to Elasticsearch and Sequences to Jdbc — each maps to any object or custom field on the other side.
Ingest pipelines Server-side transforms applied to documents as a sync writes them. Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. Ingest pipelines is specific to Elasticsearch and Tables to Jdbc — each maps to any object or custom field on the other side.
Index templates Reusable settings and mappings applied automatically to new indices a sync creates. Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. Index templates is specific to Elasticsearch and Views to Jdbc — each maps to any object or custom field on the other side.
Indices Target containers for synced records; each holds a table-like collection of JSON documents. Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. Indices is specific to Elasticsearch and Columns to Jdbc — each maps to any object or custom field on the other side.

How changes propagate between Elasticsearch and Jdbc

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.

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

Jdbc Elasticsearch Interval-based propagation

DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.

DeliveryEach detected change is written to Elasticsearch through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Elasticsearch: No fixed request quota; throughput is bounded by cluster sizing, thread pools, and bulk queue capacity.
  • Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
What ships with Elasticsearch ⇄ Jdbc

Connect Elasticsearch and Jdbc for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Elasticsearch–Jdbc connection.

Real-time

Two-way sync

Changes in Elasticsearch or Jdbc instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Elasticsearch or Jdbc 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 Elasticsearch or Jdbc record.

Observability

Monitoring

Track your Elasticsearch ⇄ Jdbc sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Elasticsearch and Jdbc.

How the Elasticsearch and Jdbc connectors work

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

Jdbc

Integration surface
JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
Authentication
A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
Change detection
No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks.
Capabilities
read · write
Rate limits
No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
How it works

How to connect Elasticsearch to Jdbc — 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 Elasticsearch and Jdbc 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
    Elasticsearch connected
    Jdbc connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Elasticsearch and Jdbc 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 · Elasticsearch ⇄ Jdbc
    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
    Elasticsearch Jdbc
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Elasticsearch and Jdbc 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
SOC 2 Type II
ISO 27001
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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 445 integrations available for Elasticsearch and Jdbc.

Popular · 4 of 445
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