Two-way sync
Changes in Apache Druid or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.
Keep Apache Druid and Cloudera Data Platform 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 Cloudera Data Platform 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 | Cloudera Data Platform objects | |
|---|---|---|
| Metrics Numeric columns, often pre-aggregated at ingestion via rollup. | Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. | |
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Impala tables The same metastore tables served through Impala for lower-latency SQL reads. | |
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. | |
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. | |
| Datasources The table-like unit of storage and querying, the main target of reads and ingestion. | Views SQL views that can present curated, sync-ready projections of raw lake data. | |
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Cloudera Data Platform connection.
Changes in Apache Druid or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Apache Druid or Cloudera Data Platform 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 Cloudera Data Platform record.
Track your Apache Druid ⇄ Cloudera Data Platform sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Apache Druid and Cloudera Data Platform.
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 Cloudera Data Platform 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 Cloudera Data Platform 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 Cloudera Data Platform: 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.
Common patterns for Apache Druid and Cloudera Data Platform: 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. Cloudera Data Platform: JDBC/ODBC over Hive and Impala SQL endpoints, plus REST management APIs. Authentication: Kerberos, LDAP, or workload user credentials, often brokered through the Knox gateway. Stacksync manages authentication, retries, and rate limits on both sides.
Apache Druid: Druid stores data in immutable, time-partitioned segments; there is no row-level update path, so writes happen through ingestion and reprocessing rather than upserts. Cloudera Data Platform: Access is commonly brokered by Apache Knox and secured with Kerberos or LDAP, which integration tooling must support. Stacksync's field mapping accounts for these differences between Apache Druid and Cloudera Data Platform 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 Cloudera Data Platform 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 Cloudera Data Platform connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Druid–Cloudera Data Platform integration in-house.
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 Cloudera Data Platform.