Two-way sync
Changes in Cloudera Data Platform or SingleStore instantly reflect in both systems. No stale data, no manual imports.
Keep Cloudera Data Platform and SingleStore 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 SingleStore'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 SingleStore where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in SingleStore sync into Cloudera Data Platform in real time, and result tables in Cloudera Data Platform sync back into SingleStore, with schema and type mapping between the two systems handled for you.
Rows from SingleStore 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 SingleStore, 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.
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 | SingleStore objects | |
|---|---|---|
| Partitions Table partitions (often by date) that incremental extraction jobs use to scope reads. | Tables (rowstore and columnstore) Primary read/write target; storage type affects whether a table suits point lookups or scans. | |
| Object store / HDFS files Underlying Parquet or ORC files on HDFS or cloud storage backing the tables. | Views Read-only projections used as curated sync sources. | |
| Databases Logical namespaces in the shared Hive Metastore that group tables for access control and syncs. | Reference Tables Small tables replicated to every node, often used for dimension data in syncs. | |
| Hive tables Warehouse tables queried over JDBC/ODBC; classic managed tables are append-oriented. | Pipelines Native ingestion jobs from Kafka or object storage that coexist with external syncs. | |
| Impala tables The same metastore tables served through Impala for lower-latency SQL reads. | Stored Procedures Existing logic sometimes invoked on write paths. | |
| Kudu tables Storage engine tables that support row-level inserts, updates, and deletes. | Indexes and Shard Keys Determine data distribution and lookup speed for sync match keys. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Cloudera Data Platform–SingleStore connection.
Changes in Cloudera Data Platform or SingleStore instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Cloudera Data Platform or SingleStore 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 SingleStore record.
Track your Cloudera Data Platform ⇄ SingleStore sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Cloudera Data Platform and SingleStore.
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 SingleStore 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 SingleStore 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 SingleStore: 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.
Change detection on Cloudera Data Platform: Polling via SQL on timestamp or partition columns; no consumer-facing change feed. On SingleStore: Polling on timestamp or watermark columns; the platform also provides change-observation features in recent versions. 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: Hive tables, Impala tables, Kudu tables, Iceberg tables, plus custom fields where Cloudera Data Platform exposes them. On the SingleStore side: Views, Reference Tables, Pipelines, Stored Procedures. 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 SingleStore: Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed; Fresh analytics without loading windows. Rows from SingleStore land in Cloudera Data Platform as they change, replacing hand-built CDC and batch extract jobs.
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. SingleStore: SQL over the MySQL wire protocol; an HTTP Data API is also available for SQL over REST. 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 Cloudera Data Platform and SingleStore.