Real-time
Two-way sync
Changes in Apache Druid or Postgres Heroku instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Apache Druid to Postgres Heroku integration — real-time, two-way sync
Keep Apache Druid 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.
- 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
Case study
Migrated from Mulesoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Apache Druid and Postgres Heroku
Connect Postgres Heroku and Apache Druid 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 Postgres Heroku's rows in Apache Druid, 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 Postgres Heroku where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Postgres Heroku sync into Apache Druid in real time, and result tables in Apache Druid sync back into Postgres Heroku, with schema and type mapping between the two systems handled for you.
Common use cases
- Feed operational records into Druid via batch ingestion so analysts get interactive slice-and-dice on fresh data.
- Sync Druid query results into a warehouse to combine real-time aggregates with historical models.
- Keep several Heroku app databases aligned with one system of record
- Reflect billing and subscription records into the app database so product logic reads local rows
Operational data in the warehouse, minus the pipeline
Rows from Postgres Heroku land in Apache Druid as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Apache Druid sync into Postgres Heroku, 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 Apache Druid 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.
| Apache Druid objects | Postgres Heroku objects | |
|---|---|---|
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Follower Databases Heroku-managed read replicas usable as low-impact sync sources. | |
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Tables Standard Postgres tables; the primary two-way sync target for app data. | |
| Datasources The table-like unit of storage and querying, the main target of reads and ingestion. | Views Read-side projections exposed to outbound syncs. | |
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Materialized Views Precomputed result sets synced outward on refresh. | |
| Dimensions String and categorical columns used for filtering and grouping in synced queries. | Schemas Namespaces that scope which tables a sync reads and writes. | |
| Metrics Numeric columns, often pre-aggregated at ingestion via rollup. | Primary and Unique Keys Match keys for idempotent upserts from connected systems. |
What ships with Apache Druid ⇄ Postgres Heroku
Connect Apache Druid and Postgres Heroku for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Postgres Heroku connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Druid 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 Apache Druid or Postgres Heroku record.
Observability
Monitoring
Track your Apache Druid ⇄ 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 Apache Druid and Postgres Heroku.
How the Apache Druid and Postgres Heroku connectors work
Apache Druid
- Integration surface
- REST API (SQL over HTTP and native JSON queries); JDBC via Avatica
- Authentication
- Deployment-dependent: basic authentication or an authenticator extension; often fronted by a proxy
- Change detection
- Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates
- Capabilities
- read · write
- Rate limits
- No fixed API quotas; query concurrency is bounded by broker and historical node 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 Apache Druid 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 Apache Druid 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.
Apache Druid
connected
Postgres Heroku
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Druid 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 · Apache Druid ⇄ 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.
Apache Druid Postgres HerokuCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Druid and Postgres Heroku integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Druid and Postgres Heroku: authenticate both systems, choose the objects to sync (such as Apache Druid's Lookups and Tasks), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Apache Druid: Streaming ingestion from Kafka or Kinesis is managed by supervisors designed to provide exactly-once ingestion semantics. 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 Apache Druid and Postgres Heroku without custom code.
-
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 Apache Druid and Postgres Heroku records are not retained after a sync operation.
-
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Druid and Postgres Heroku connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Druid–Postgres Heroku integration in-house.
-
Yes — Stacksync ships production-grade connectors for both Apache Druid and Postgres Heroku. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
-
Change detection on Apache Druid: Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates. 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 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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Apache Druid and Postgres Heroku.
Popular · 8 of 386
-
Apache Druid ⇄ AWS Aurora MySQL - Apache Druid ⇄ AWS Aurora PostgreSQL
-
Apache Druid ⇄ MongoDB -
Apache Druid ⇄ CockroachDB -
Postgres Heroku ⇄ Starburst Enterprise -
Postgres Heroku ⇄ StarRocks -
Postgres Heroku ⇄ Teradata Vantage -
Postgres Heroku ⇄ Tinybird
No integrations match .
