Real-time
Two-way sync
Changes in Apache Cassandra or BigQuery instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
Apache Cassandra to BigQuery integration — real-time, two-way sync
Keep Apache Cassandra and BigQuery 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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Why teams connect Apache Cassandra and BigQuery
Connect Apache Cassandra and BigQuery 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 BigQuery, 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 BigQuery in real time, and result tables in BigQuery sync back into Apache Cassandra, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
- 02 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
- 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 BigQuery and keep Apache Cassandra focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Apache Cassandra land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in BigQuery sync into Apache Cassandra, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Apache Cassandra and BigQuery
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 | BigQuery objects | How this pairing syncs | |
|---|---|---|---|
| Tables Wide-column tables addressed by partition key, the unit of row-level sync. | Tables The syncable unit: only tables can be synced per the Stacksync docs. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Secondary Indexes Optional indexes that allow filtered reads outside the partition key. | Partitioned tables Synced like regular tables; partition columns map to target fields. | Secondary Indexes is specific to Apache Cassandra and Partitioned tables to BigQuery — 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. | Clustered tables Supported; clustering is transparent to the sync. | User-Defined Types is specific to Apache Cassandra and Clustered tables to BigQuery — each maps to any object or custom field on the other side. | |
| Collections List, set, and map columns handled with type-aware field mapping. | Datasets Organizational container — you pick which dataset’s tables to sync. | Collections is specific to Apache Cassandra and Datasets to BigQuery — each maps to any object or custom field on the other side. | |
| Counters Increment-only counter columns, usually read-only in syncs. | Projects Connection scope: the service account grants access per project. | Counters is specific to Apache Cassandra and Projects to BigQuery — each maps to any object or custom field on the other side. |
How changes propagate between Apache Cassandra and BigQuery
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 BigQuery 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 BigQuery as a row-level write, with types converted between the two schemas.
BigQuery Apache Cassandra Sub-second propagation
DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").
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.
- BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
What ships with Apache Cassandra ⇄ BigQuery
Connect Apache Cassandra and BigQuery for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Cassandra–BigQuery connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Cassandra or BigQuery 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 BigQuery record.
Observability
Monitoring
Track your Apache Cassandra ⇄ BigQuery 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 BigQuery.
How the Apache Cassandra and BigQuery 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
BigQuery
- Integration surface
- GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
- Authentication
- Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
- Change detection
- Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
- Capabilities
- read · write · CDC
- Rate limits
- Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
How it works
How to connect Apache Cassandra to BigQuery — 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 BigQuery 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
BigQuery
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Cassandra and BigQuery 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 ⇄ BigQueryCustomers 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 BigQueryCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Cassandra and BigQuery integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Cassandra and BigQuery: authenticate both systems, choose the objects to sync (such as Apache Cassandra's Tables and Secondary Indexes), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the BigQuery side: Partitioned tables, Clustered tables, Datasets, Projects, plus custom fields where BigQuery 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 BigQuery: 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 BigQuery 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. BigQuery: GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs. Authentication: Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver. Stacksync manages authentication, retries, and rate limits on both sides.
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BigQuery: Google quota of 1,500 table modifications per BigQuery table per day (DELETE, INSERT, MERGE, TRUNCATE TABLE, UPDATE). Apache Cassandra: Data modeling is query-first and denormalized: tables are designed around partition keys, and there are no joins, so syncs address rows by partition and clustering keys. Stacksync's field mapping accounts for these differences between Apache Cassandra and BigQuery 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
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