Real-time
Two-way sync
Changes in Apache Cassandra or Yellowbrick instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
Apache Cassandra to Yellowbrick integration — real-time, two-way sync
Keep Apache Cassandra and Yellowbrick in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Apache Cassandra and Yellowbrick
Connect Apache Cassandra and Yellowbrick with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.
Operational databases and analytical warehouses want the same data at different moments. Analysts want Apache Cassandra's rows in Yellowbrick, 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 Apache Cassandra where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Apache Cassandra sync into Yellowbrick in real time, and result tables in Yellowbrick sync back into Apache Cassandra, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Sync CRM and marketing data into Yellowbrick tables so it can be joined with large fact tables for enterprise BI.
- 02 Land ERP transactional extracts in Yellowbrick for finance and supply-chain analytics.
- 03 Replicate high-volume event or profile tables from Cassandra into a warehouse for analytics that CQL cannot express.
- 04 Sync customer profile data between Cassandra and a CRM so operational apps and sales tools agree.
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in Yellowbrick and keep Apache Cassandra focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Apache Cassandra land in Yellowbrick as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Yellowbrick sync into Apache Cassandra, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Apache Cassandra and Yellowbrick
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 Cassandra objects | Yellowbrick objects | How this pairing syncs | |
|---|---|---|---|
| Tables Wide-column tables addressed by partition key, the unit of row-level sync. | Tables Columnar MPP tables; the primary targets for warehouse syncs. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Partitions and Rows Records located by partition and clustering keys during reads and upserts. | Schemas Namespaces used to organize synced datasets by source or domain. | Partitions and Rows is specific to Apache Cassandra and Schemas to Yellowbrick — each maps to any object or custom field on the other side. | |
| Materialized Views Server-maintained denormalized views; considered experimental and disabled by default in recent releases. | Views Logical views used to shape reads for BI and downstream syncs. | Materialized Views is specific to Apache Cassandra and Views to Yellowbrick — each maps to any object or custom field on the other side. | |
| Secondary Indexes Optional indexes that allow filtered reads outside the partition key. | Users and Roles Access-control objects that govern what a sync service account can read and write. | Secondary Indexes is specific to Apache Cassandra and Users and Roles to Yellowbrick — each maps to any object or custom field on the other side. | |
| User-Defined Types Composite column types that syncs must flatten or map to structured fields. | Databases Top-level containers for schemas and tables. | User-Defined Types is specific to Apache Cassandra and Databases to Yellowbrick — each maps to any object or custom field on the other side. |
How changes propagate between Apache Cassandra and Yellowbrick
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
Apache Cassandra Yellowbrick Sub-second propagation
DetectionChanges in Apache Cassandra are captured at the source via change data capture — no polling loop against its API. Commit-log based CDC on tables with CDC enabled, or polling using writetime metadata and timestamp columns.
DeliveryEach detected change is applied to Yellowbrick as a row-level write, with types converted between the two schemas.
Yellowbrick Apache Cassandra Interval-based propagation
DetectionStacksync polls Yellowbrick for changes on an incremental schedule, reading only records changed since the previous pass. Polling on timestamp columns.
DeliveryEach detected change is written to Apache Cassandra through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Apache Cassandra: No API quotas; throughput is governed by cluster capacity and consistency-level choices.
- Yellowbrick: No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
What ships with Apache Cassandra ⇄ Yellowbrick
Connect Apache Cassandra and Yellowbrick for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Cassandra–Yellowbrick connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Cassandra or Yellowbrick data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
At scale
Event queues
Handle millions of events per minute without losing a single Apache Cassandra or Yellowbrick record.
Observability
Monitoring
Track your Apache Cassandra ⇄ Yellowbrick sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Apache Cassandra and Yellowbrick.
How the Apache Cassandra and Yellowbrick connectors work
Apache Cassandra
- Integration surface
- CQL over the Cassandra native binary protocol
- Authentication
- Database credentials (password authenticator); TLS and role-based grants where configured
- Change detection
- Commit-log based CDC on tables with CDC enabled, or polling using writetime metadata and timestamp columns
- Capabilities
- read · write · CDC
- Rate limits
- No API quotas; throughput is governed by cluster capacity and consistency-level choices
Yellowbrick
- Integration surface
- SQL wire protocol (PostgreSQL-compatible) with JDBC/ODBC drivers; bulk loading via the ybload utility
- Authentication
- Database credentials, with LDAP and Kerberos options in enterprise deployments
- Change detection
- Polling on timestamp columns; no exposed transaction-log CDC
- Capabilities
- read · write
- Rate limits
- No API rate limits; throughput depends on cluster sizing, and bulk loads should use ybload rather than row-by-row inserts.
How it works
How to connect Apache Cassandra to Yellowbrick — three steps, no code
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.
- 01
Connect your apps
Authenticate Apache Cassandra and Yellowbrick with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Apache Cassandra
connected
Yellowbrick
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Cassandra and Yellowbrick 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.
objects · Apache Cassandra ⇄ YellowbrickCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Apache Cassandra YellowbrickCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Cassandra and Yellowbrick integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Cassandra and Yellowbrick: authenticate both systems, choose the objects to sync (such as Apache Cassandra's Tables and Partitions and Rows), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Yellowbrick side: Databases, Schemas, Tables, Views, plus custom fields where Yellowbrick exposes them. On the Apache Cassandra side: Counters, Keyspaces, Tables, Partitions and Rows. Stacksync auto-detects both schemas and converts types between the two systems.
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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.
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Common patterns for Apache Cassandra and Yellowbrick: Offload heavy reads; Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed. Point analytical queries at the synced copy in Yellowbrick and keep Apache Cassandra focused on its operational workload.
-
Apache Cassandra: CQL over the Cassandra native binary protocol. Authentication: Database credentials (password authenticator); TLS and role-based grants where configured. Yellowbrick: SQL wire protocol (PostgreSQL-compatible) with JDBC/ODBC drivers; bulk loading via the ybload utility. Authentication: Database credentials, with LDAP and Kerberos options in enterprise deployments. Stacksync manages authentication, retries, and rate limits on both sides.
-
Yellowbrick: The front end is PostgreSQL-compatible, so standard Postgres drivers and SQL tooling connect without custom clients. Apache Cassandra: CDC is enabled per table and surfaces changes through commit-log segments, which is how log-based connectors consume Cassandra changes. Stacksync's field mapping accounts for these differences between Apache Cassandra and Yellowbrick without custom code.
SECURITY
Security teams trust Stacksync
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS
SSO & SCIM
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Alerts
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Secure connection options
Securely connects to your systems with:
Related integrations
Every pair below is a real-time, two-way sync. Search all 357 integrations available for Apache Cassandra and Yellowbrick.
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