Two-way sync
Changes in Apache Druid or Greenplum instantly reflect in both systems. No stale data, no manual imports.
Keep Apache Druid and Greenplum in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.
Stacksync syncs tables between Apache Druid and Greenplum continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
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 | Greenplum objects | |
|---|---|---|
| Metrics Numeric columns, often pre-aggregated at ingestion via rollup. | External tables Reference external files for bulk load paths alongside row-level syncs. | |
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Rows Read and written by key; distribution keys determine where rows live. | |
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Databases Top-level containers that scope a sync connection. | |
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Schemas Namespace tables and control which objects a sync can see. | |
| Datasources The table-like unit of storage and querying, the main target of reads and ingestion. | Tables Heap or append-optimized tables mapped directly to sync targets. | |
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Partitions Large tables are commonly partitioned by date, which shapes incremental reads. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Greenplum connection.
Changes in Apache Druid or Greenplum instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Apache Druid or Greenplum data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Apache Druid or Greenplum record.
Track your Apache Druid ⇄ Greenplum sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Apache Druid and Greenplum.
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.
Authenticate Apache Druid and Greenplum with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the Apache Druid and Greenplum 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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between Apache Druid and Greenplum: authenticate both systems, choose the objects to sync (such as Apache Druid's Metrics and Ingestion Supervisors), map fields visually, and changes propagate both ways in milliseconds — no code required.
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 Greenplum: Polling with timestamp or key-based cursors; Greenplum does not expose logical-decoding CDC. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Apache Druid side: Tasks, Datasources, Segments, Dimensions, plus custom fields where Apache Druid exposes them. On the Greenplum side: Rows, Databases, Schemas, Tables. Stacksync auto-detects both schemas and converts types between the two systems.
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
Common patterns for Apache Druid and Greenplum: Migration without a big bang; Serve tools that only connect to one platform; Shared datasets across teams. When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
Apache Druid: 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. Greenplum: PostgreSQL wire protocol (libpq), plus JDBC/ODBC drivers. Authentication: Database credentials. Stacksync manages authentication, retries, and rate limits on both sides.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Apache Druid and Greenplum.