Two-way sync
Changes in Cloudera Data Platform or TimescaleDB instantly reflect in both systems. No stale data, no manual imports.
Keep Cloudera Data Platform and TimescaleDB in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Operational databases and analytical warehouses want the same data at different moments. Analysts want TimescaleDB's rows in Cloudera Data Platform, 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 TimescaleDB where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in TimescaleDB sync into Cloudera Data Platform in real time, and result tables in Cloudera Data Platform sync back into TimescaleDB, with schema and type mapping between the two systems handled for you.
Aggregates or model outputs computed in Cloudera Data Platform sync into TimescaleDB, where whatever reads from that database gets them without querying the warehouse.
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Point analytical queries at the synced copy in Cloudera Data Platform and keep TimescaleDB focused on its operational workload.
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.
| Cloudera Data Platform objects | TimescaleDB objects | |
|---|---|---|
| Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. | Regular PostgreSQL Tables Relational reference data such as devices, tenants, or accounts synced alongside the series data. | |
| Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. | Views Standard SQL views used to shape or filter data for consumers. | |
| Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. | Schemas Postgres namespaces used to separate synced datasets by team or environment. | |
| Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. | Hypertables Time-partitioned tables that hold the main time-series data; the primary read and write target in syncs. | |
| Impala tables The same metastore tables served through Impala for lower-latency SQL reads. | Chunks Time-bounded partitions of a hypertable; syncs read and write through the parent hypertable and never address chunks directly. | |
| Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. | Continuous Aggregates Incrementally maintained rollups that serve as pre-aggregated read sources for downstream systems. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–TimescaleDB connection.
Changes in Cloudera Data Platform or TimescaleDB instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Cloudera Data Platform or TimescaleDB data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Cloudera Data Platform or TimescaleDB record.
Track your Cloudera Data Platform ⇄ TimescaleDB sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Cloudera Data Platform and TimescaleDB.
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 Cloudera Data Platform and TimescaleDB 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 Cloudera Data Platform and TimescaleDB 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 Cloudera Data Platform and TimescaleDB: authenticate both systems, choose the objects to sync (such as Cloudera Data Platform's Partitions and Object store / HDFS files), map fields visually, and changes propagate both ways in milliseconds — no code required.
Yes — Stacksync ships production-grade connectors for both Cloudera Data Platform and TimescaleDB. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Cloudera Data Platform: Polling via SQL on timestamp or partition columns; no consumer-facing change feed. On TimescaleDB: Log-based capture via PostgreSQL logical decoding where the deployment allows it — hypertable changes surface on the underlying chunk tables and must be remapped to the parent — or timestamp-based polling on time columns; regular Postgres tables replicate through standard logical replication. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Cloudera Data Platform side: Object store / HDFS files, Databases, Hive tables, Impala tables, plus custom fields where Cloudera Data Platform exposes them. On the TimescaleDB side: Continuous Aggregates, Regular PostgreSQL Tables, Views, Schemas. 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 Cloudera Data Platform and TimescaleDB: Serve warehouse results at database speed; Fresh analytics without loading windows; Offload heavy reads. Aggregates or model outputs computed in Cloudera Data Platform sync into TimescaleDB, where whatever reads from that database gets them without querying the warehouse.
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 Cloudera Data Platform and TimescaleDB.