Real-time
Two-way sync
Changes in Apache Cassandra or Apache Hive instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
Apache Cassandra to Apache Hive integration — real-time, two-way sync
Keep Apache Cassandra and Apache Hive 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 Hive
Connect Apache Cassandra and Apache Hive 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 Hive, 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 Hive in real time, and result tables in Apache Hive sync back into Apache Cassandra, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Publish Hive aggregate tables to a faster serving database for dashboards.
- 02 Bridge a legacy Hadoop warehouse to a cloud warehouse during migration by syncing tables continuously.
- 03 Consolidate data from multiple keyspaces or clusters into one reporting store.
- 04 Replicate high-volume event or profile tables from Cassandra into a warehouse for analytics that CQL cannot express.
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 Hive and keep Apache Cassandra focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Apache Cassandra land in Apache Hive as they change, replacing hand-built CDC and batch extract jobs.
What you can sync between Apache Cassandra and Apache Hive
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 Hive objects | How this pairing syncs | |
|---|---|---|---|
| Materialized Views Server-maintained denormalized views; considered experimental and disabled by default in recent releases. | Materialized Views Precomputed results available in newer Hive versions for faster reads. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Tables Wide-column tables addressed by partition key, the unit of row-level sync. | Databases Metastore namespaces that scope tables and grants. | Tables is specific to Apache Cassandra and Databases to Apache Hive — 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. | Managed Tables Tables whose data lifecycle Hive controls, used as warehouse destinations. | Partitions and Rows is specific to Apache Cassandra and Managed Tables to Apache Hive — each maps to any object or custom field on the other side. | |
| Secondary Indexes Optional indexes that allow filtered reads outside the partition key. | External Tables Tables over existing files in HDFS or object storage, read without moving data. | Secondary Indexes is specific to Apache Cassandra and External Tables to Apache Hive — 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. | Partitions Directory-mapped subsets (often by date) that bound incremental sync reads. | User-Defined Types is specific to Apache Cassandra and Partitions to Apache Hive — each maps to any object or custom field on the other side. | |
| Collections List, set, and map columns handled with type-aware field mapping. | Views Logical views readable as modeled sources. | Collections is specific to Apache Cassandra and Views to Apache Hive — each maps to any object or custom field on the other side. |
How changes propagate between Apache Cassandra and Apache Hive
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 Hive 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 Hive as a row-level write, with types converted between the two schemas.
Apache Hive Apache Cassandra Interval-based propagation
DetectionStacksync polls Apache Hive for changes on an incremental schedule, reading only records changed since the previous pass. Polling on partition values or 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.
- Apache Hive: No API quotas; query latency reflects the batch-oriented execution engine underneath.
What ships with Apache Cassandra ⇄ Apache Hive
Connect Apache Cassandra and Apache Hive for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Cassandra–Apache Hive connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Cassandra or Apache Hive 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 Hive record.
Observability
Monitoring
Track your Apache Cassandra ⇄ Apache Hive 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 Hive.
How the Apache Cassandra and Apache Hive 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 Hive
- Integration surface
- SQL (HiveQL) over JDBC/ODBC via HiveServer2 (Thrift)
- Authentication
- Deployment-dependent: Kerberos, LDAP, or username/password
- Change detection
- Polling on partition values or timestamp columns; no general-purpose change log for external consumers
- Capabilities
- read · write
- Rate limits
- No API quotas; query latency reflects the batch-oriented execution engine underneath
How it works
How to connect Apache Cassandra to Apache Hive — 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 Hive 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 Hive
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Cassandra and Apache Hive 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 HiveCustomers 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 HiveCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Cassandra and Apache Hive integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Apache Cassandra and Apache Hive: authenticate both systems, choose the objects to sync (such as Apache Cassandra's Materialized Views and Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Common patterns for Apache Cassandra and Apache Hive: 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 Hive: SQL (HiveQL) over JDBC/ODBC via HiveServer2 (Thrift). Authentication: Deployment-dependent: Kerberos, LDAP, or username/password. Stacksync manages authentication, retries, and rate limits on both sides.
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Apache Hive: Partitioned tables map partitions to directory paths, making partition values a natural incremental-sync boundary. Apache Cassandra: Every write carries a timestamp (writetime) per cell, which integrations can use for incremental extraction and conflict resolution. Stacksync's field mapping accounts for these differences between Apache Cassandra and Apache Hive 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 Hive records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Cassandra and Apache Hive connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Cassandra–Apache Hive integration in-house.
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 366 integrations available for Apache Cassandra and Apache Hive.
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