Two-way sync
Changes in Apache Cassandra or Rillet instantly reflect in both systems. No stale data, no manual imports.
Keep Apache Cassandra and Rillet in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Engineers need finance data more often than finance systems make it easy to get: for internal tools, reporting services, or logic that reacts to invoices and payments. Working through the vendor API means rate limits, pagination, and glue code that has to be maintained forever.
Stacksync mirrors Journal Entry, Invoice Payment, Charge, Reimbursement from Rillet into Counters, Keyspaces, Tables, Partitions and Rows in Apache Cassandra and keeps the two in sync bi-directionally and in real time. Your services read finance records with normal queries against Apache Cassandra, and rows your code writes or updates flow back into Rillet with validation, so the finance system stays the system of record.
Changes in Rillet appear in Apache Cassandra as row changes, so you can trigger downstream logic with the database tooling you already use.
Customers, invoices, and payments from Rillet live in Apache Cassandra as regular tables or collections your team can join, index, and query.
Build dashboards and back-office tools directly on Apache Cassandra; Stacksync handles the API calls, rate limits, and retries against Rillet.
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 | Rillet objects | How this pairing syncs | |
|---|---|---|---|
| Tables Wide-column tables addressed by partition key, the unit of row-level sync. | Account Synced with incremental and full sync per the Stacksync docs. | Tables is specific to Apache Cassandra and Account to Rillet — each maps to any object or custom field on the other side. | |
| Partitions and Rows Records located by partition and clustering keys during reads and upserts. | Customer Synced with incremental and full sync per the Stacksync docs. | Partitions and Rows is specific to Apache Cassandra and Customer to Rillet — 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. | Vendor Synced with incremental and full sync per the Stacksync docs. | Materialized Views is specific to Apache Cassandra and Vendor to Rillet — each maps to any object or custom field on the other side. | |
| Secondary Indexes Optional indexes that allow filtered reads outside the partition key. | Invoice Synced with incremental and full sync per the Stacksync docs. | Secondary Indexes is specific to Apache Cassandra and Invoice to Rillet — 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. | Credit Memo Synced with incremental and full sync per the Stacksync docs. | User-Defined Types is specific to Apache Cassandra and Credit Memo to Rillet — each maps to any object or custom field on the other side. | |
| Collections List, set, and map columns handled with type-aware field mapping. | Bill Synced with incremental and full sync per the Stacksync docs. | Collections is specific to Apache Cassandra and Bill to Rillet — each maps to any object or custom field on the other side. |
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.
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 written to Rillet through its API, with automatic retries and rate-limit backoff.
DetectionRillet pushes changes as they happen — webhook events backed by change data capture. Near real-time updates via change tracking.
DeliveryEach detected change is written to Apache Cassandra through its API, with automatic retries and rate-limit backoff.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Cassandra–Rillet connection.
Changes in Apache Cassandra or Rillet instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Apache Cassandra or Rillet data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Apache Cassandra or Rillet record.
Track your Apache Cassandra ⇄ Rillet sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Apache Cassandra and Rillet.
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 Apache Cassandra and Rillet 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 Apache Cassandra and Rillet 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 Apache Cassandra and Rillet: 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.
Yes — Stacksync ships production-grade connectors for both Apache Cassandra and Rillet. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on Apache Cassandra: Commit-log based CDC on tables with CDC enabled, or polling using writetime metadata and timestamp columns. On Rillet: Near real-time updates via change tracking; incremental sync per object, with delete detection (some objects checked every 1h or 4h). Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Rillet side: Journal Entry, Invoice Payment, Charge, Reimbursement, plus custom fields where Rillet 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.
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 Apache Cassandra and Rillet: React to financial events; Query finance data like any other data; Internal tools without API plumbing. Changes in Rillet appear in Apache Cassandra as row changes, so you can trigger downstream logic with the database tooling you already use.
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.
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Every pair below is a real-time, two-way sync. Search all 353 integrations available for Apache Cassandra and Rillet.