Real-time
Two-way sync
Changes in Apache Cassandra or Apache Pinot instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
Apache Cassandra to Apache Pinot integration — real-time, two-way sync
Keep Apache Cassandra and Apache Pinot in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Apache Cassandra and Apache Pinot
Connect Apache Cassandra and Apache Pinot 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 Apache Pinot, 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 Apache Pinot in real time, and result tables in Apache Pinot sync back into Apache Cassandra, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Query per-account usage metrics from Pinot and sync them into CRM fields so sales sees product activity.
- 02 Push reference and dimension data into Pinot via batch segment loads to enrich event queries.
- 03 Write attributes computed elsewhere (scores, preferences) back into Cassandra tables serving low-latency reads.
- 04 Feed Cassandra change streams into search indexes or caches that must track the source of truth.
Common sync patterns
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Offload heavy reads
Point analytical queries at the synced copy in Apache Pinot and keep Apache Cassandra focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Apache Cassandra land in Apache Pinot as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between Apache Cassandra and Apache Pinot
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 | Apache Pinot objects | How this pairing syncs | |
|---|---|---|---|
| Tables Wide-column tables addressed by partition key, the unit of row-level sync. | Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Keyspaces Top-level namespaces with replication settings that scope a sync connection. | Offline Tables Batch-loaded tables merged with real-time data at query time. | Keyspaces is specific to Apache Cassandra and Offline Tables to Apache Pinot — 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. | Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. | Partitions and Rows is specific to Apache Cassandra and Indexes to Apache Pinot — 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. | Tenants Logical groupings that isolate workloads on shared clusters. | Materialized Views is specific to Apache Cassandra and Tenants to Apache Pinot — each maps to any object or custom field on the other side. | |
| Secondary Indexes Optional indexes that allow filtered reads outside the partition key. | Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. | Secondary Indexes is specific to Apache Cassandra and Schemas to Apache Pinot — 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. | Segments Immutable data files that batch ingestion uploads and the cluster serves. | User-Defined Types is specific to Apache Cassandra and Segments to Apache Pinot — each maps to any object or custom field on the other side. |
How changes propagate between Apache Cassandra and Apache Pinot
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 Apache Pinot 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 Apache Pinot as a row-level write, with types converted between the two schemas.
Apache Pinot Apache Cassandra Interval-based propagation
DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.
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.
- Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
What ships with Apache Cassandra ⇄ Apache Pinot
Connect Apache Cassandra and Apache Pinot for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Cassandra–Apache Pinot connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Cassandra or Apache Pinot 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 Apache Pinot record.
Observability
Monitoring
Track your Apache Cassandra ⇄ Apache Pinot 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 Apache Pinot.
How the Apache Cassandra and Apache Pinot 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
Apache Pinot
- Integration surface
- REST API (SQL queries via the broker; administration via the controller); JDBC client available
- Authentication
- Deployment-dependent: HTTP basic authentication or token-based auth where enabled
- Change detection
- Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
- Capabilities
- read · write
- Rate limits
- No fixed API quotas; query throughput depends on broker and server sizing
How it works
How to connect Apache Cassandra to Apache Pinot — 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 Apache Pinot 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
Apache Pinot
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Cassandra and Apache Pinot 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 ⇄ Apache PinotCustomers 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 Apache PinotCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Cassandra and Apache Pinot integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Apache Cassandra and Apache Pinot: authenticate both systems, choose the objects to sync (such as Apache Cassandra's Tables and Keyspaces), map fields visually, and changes propagate both ways in milliseconds — no code required.
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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 Apache Pinot: Fresh analytics without loading windows; Offload heavy reads; Operational data in the warehouse, minus the pipeline. Because changes stream continuously, analysts query current data instead of waiting for last night's load.
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Apache Cassandra: CQL over the Cassandra native binary protocol. Authentication: Database credentials (password authenticator); TLS and role-based grants where configured. Apache Pinot: REST API (SQL queries via the broker; administration via the controller); JDBC client available. Authentication: Deployment-dependent: HTTP basic authentication or token-based auth where enabled. Stacksync manages authentication, retries, and rate limits on both sides.
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Apache Pinot: Pinot separates offline and real-time tables and merges them at query time through the broker, so one logical table can span batch history and fresh stream data. 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 Apache Pinot without custom code.
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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 Apache Cassandra and Apache Pinot records are not retained after a sync operation.
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 363 integrations available for Apache Cassandra and Apache Pinot.
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