Two-way sync
Changes in Amazon Aurora or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.
Keep Amazon Aurora 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.
Operational databases and analytical warehouses want the same data at different moments. Analysts want Amazon Aurora'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 Amazon Aurora where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Amazon Aurora sync into Cloudera Data Platform in real time, and result tables in Cloudera Data Platform sync back into Amazon Aurora, with schema and type mapping between the two systems handled for you.
Point analytical queries at the synced copy in Cloudera Data Platform and keep Amazon Aurora focused on its operational workload.
Rows from Amazon Aurora land in Cloudera Data Platform as they change, replacing hand-built CDC and batch extract jobs.
Aggregates or model outputs computed in Cloudera Data Platform sync into Amazon Aurora, where whatever reads from that database gets them without querying the warehouse.
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.
| Amazon Aurora objects | Cloudera Data Platform objects | |
|---|---|---|
| Read Replicas Reader endpoints that syncs can target to keep load off the writer. | Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. | |
| Databases Logical databases within a cluster that scope a sync connection. | Iceberg tables Open table format tables in newer CDP versions, with snapshot metadata usable for incremental reads. | |
| Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. | Views SQL views that can present curated, sync-ready projections of raw lake data. | |
| Tables Relational tables synced bi-directionally at row level. | Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. | |
| Views Read-only query-backed sources for downstream syncs. | Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. | |
| Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. | 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 Amazon Aurora–Cloudera Data Platform connection.
Changes in Amazon Aurora or Cloudera Data Platform instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Amazon Aurora 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 Amazon Aurora or Cloudera Data Platform record.
Track your Amazon Aurora ⇄ Cloudera Data Platform sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Amazon Aurora 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 Amazon Aurora 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 Amazon Aurora 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 Amazon Aurora and Cloudera Data Platform: authenticate both systems, choose the objects to sync (such as Amazon Aurora's Read Replicas and Databases), map fields visually, and changes propagate both ways in milliseconds — no code required.
Cloudera Data Platform: Tables can live in multiple storage engines with different update semantics: Kudu and Iceberg tables support row-level updates, while classic Hive tables are append-oriented. Amazon Aurora: Aurora separates compute from a shared distributed storage layer that keeps six copies of data across three Availability Zones. Stacksync's field mapping accounts for these differences between Amazon Aurora 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 Amazon Aurora 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 Amazon Aurora and Cloudera Data Platform connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Amazon Aurora–Cloudera Data Platform integration in-house.
Yes — Stacksync ships production-grade connectors for both Amazon Aurora and Cloudera Data Platform. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Amazon Aurora: Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback. On Cloudera Data Platform: Polling via SQL on timestamp or partition columns; no consumer-facing change feed. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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 Amazon Aurora and Cloudera Data Platform.