Real-time
Two-way sync
Changes in Elasticsearch or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Database
Elasticsearch to Postgres Heroku integration — real-time, two-way sync
Keep Elasticsearch and Postgres Heroku 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
Case study
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 Elasticsearch and Postgres Heroku
Keep Elasticsearch and Postgres Heroku 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 Postgres Heroku 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 Feed enriched customer records into an index used for vector or hybrid search in AI applications.
- 02 Sync CRM accounts and contacts into an Elasticsearch index to power internal search across customer records.
- 03 Reflect billing and subscription records into the app database so product logic reads local rows
- 04 Expose CRM objects as Postgres tables the Heroku application can query and join directly
Common sync patterns
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 Postgres Heroku, so each workload runs on the engine that suits it.
Migration with zero-downtime cutover
When one database is replacing the other, sync both directions during the transition and switch traffic when ready, without a freeze window.
What you can sync between Elasticsearch and Postgres Heroku
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 | Postgres Heroku objects | How this pairing syncs | |
|---|---|---|---|
| Aliases Stable read/write names that let a sync cut over between index versions without downtime. | Follower Databases Heroku-managed read replicas usable as low-impact sync sources. | Aliases is specific to Elasticsearch and Follower Databases to Postgres Heroku — 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. | Tables Standard Postgres tables; the primary two-way sync target for app data. | Data streams is specific to Elasticsearch and Tables to Postgres Heroku — 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. | Views Read-side projections exposed to outbound syncs. | Ingest pipelines is specific to Elasticsearch and Views to Postgres Heroku — 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. | Materialized Views Precomputed result sets synced outward on refresh. | Index templates is specific to Elasticsearch and Materialized Views to Postgres Heroku — 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. | Schemas Namespaces that scope which tables a sync reads and writes. | Indices is specific to Elasticsearch and Schemas to Postgres Heroku — each maps to any object or custom field on the other side. | |
| Documents The unit of sync; JSON records created, updated, and deleted by _id. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. | Documents is specific to Elasticsearch and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side. |
How changes propagate between Elasticsearch and Postgres Heroku
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 Postgres Heroku 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 Postgres Heroku as a row-level write, with types converted between the two schemas.
Postgres Heroku Elasticsearch Interval-based propagation
DetectionStacksync polls Postgres Heroku for changes on an incremental schedule, reading only records changed since the previous pass. Trigger-based capture or polling in most configurations.
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.
- Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Elasticsearch ⇄ Postgres Heroku
Connect Elasticsearch and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Elasticsearch–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Elasticsearch or Postgres Heroku 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 Postgres Heroku record.
Observability
Monitoring
Track your Elasticsearch ⇄ Postgres Heroku 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 Postgres Heroku.
How the Elasticsearch and Postgres Heroku 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
Postgres Heroku
- Integration surface
- SQL wire protocol (standard PostgreSQL)
- Authentication
- Database credentials from the Heroku DATABASE_URL config var; SSL required
- Change detection
- Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings
- Capabilities
- read · write
- Rate limits
- No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan
How it works
How to connect Elasticsearch to Postgres Heroku — 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 Elasticsearch and Postgres Heroku with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Elasticsearch
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Elasticsearch and Postgres Heroku 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 · Elasticsearch ⇄ Postgres HerokuCustomers 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.
Elasticsearch Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Elasticsearch and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Elasticsearch and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Elasticsearch's Aliases and Data streams), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Elasticsearch: Optimistic concurrency uses _seq_no and _primary_term instead of row locks, which matters when two writers touch the same document. Postgres Heroku: Heroku Postgres is standard PostgreSQL, so any Postgres client, driver, or SQL tool connects unchanged. Stacksync's field mapping accounts for these differences between Elasticsearch and Postgres Heroku without custom code.
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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 Elasticsearch and Postgres Heroku 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 Elasticsearch and Postgres Heroku connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Elasticsearch–Postgres Heroku integration in-house.
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Yes — Stacksync ships production-grade connectors for both Elasticsearch and Postgres Heroku. 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 Elasticsearch: Polling on timestamp or sequence fields; Elasticsearch does not expose a native change feed or webhooks. On Postgres Heroku: Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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 462 integrations available for Elasticsearch and Postgres Heroku.
Popular · 5 of 462
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Elasticsearch ⇄ SingleStore -
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Elasticsearch ⇄ Amazon Lightsail -
Elasticsearch ⇄ Apache Cassandra - Postgres Heroku ⇄ SingleStore
No integrations match .
