Two-way sync
Changes in BigQuery or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.
Keep BigQuery 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 BigQuery 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.
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
Bring the acquired company's warehouse data across continuously instead of through one-off dumps.
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
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.
| BigQuery objects | Cloudera Data Platform objects | |
|---|---|---|
| Clustered tables Supported; clustering is transparent to the sync. | Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Views SQL views that can present curated, sync-ready projections of raw lake data. | |
| Projects Connection scope: the service account grants access per project. | Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. | |
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. | |
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Cloudera Data Platform connection.
Changes in BigQuery or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever BigQuery 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 BigQuery or Cloudera Data Platform record.
Track your BigQuery ⇄ Cloudera Data Platform sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between BigQuery 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 BigQuery 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 BigQuery 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 BigQuery and Cloudera Data Platform: authenticate both systems, choose the objects to sync (such as BigQuery's Clustered tables and Datasets), map fields visually, and changes propagate both ways in milliseconds — no code required.
On the BigQuery side: Partitioned tables, Clustered tables, Datasets, Projects, plus custom fields where BigQuery exposes them. On the Cloudera Data Platform side: Iceberg tables, Views, Partitions, Object store / HDFS files. 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 BigQuery and Cloudera Data Platform: Shared datasets across teams; Consolidation after M&A; Migration without a big bang. Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
BigQuery: GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs. Authentication: Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver. 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.
BigQuery: BigQuery is serverless: there are no clusters or warehouses to size, and storage and compute are billed separately. 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 BigQuery and Cloudera Data Platform without custom code.
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 BigQuery and Cloudera Data Platform.