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Database ⇄ Data warehouse

Apache Cassandra to Databricks integration — real-time, two-way sync

Keep Apache Cassandra and Databricks 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 Databricks

Connect Apache Cassandra and Databricks 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 Databricks, 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 Databricks in real time, and result tables in Databricks 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 Delta tables continuously so lakehouse models work from current operational data.
  • 02 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 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 Databricks and keep Apache Cassandra focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from Apache Cassandra land in Databricks as they change, replacing hand-built CDC and batch extract jobs.

What you can sync between Apache Cassandra and Databricks

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 Databricks objects How this pairing syncs
Materialized Views Server-maintained denormalized views; considered experimental and disabled by default in recent releases. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Collections List, set, and map columns handled with type-aware field mapping. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Collections is specific to Apache Cassandra and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Counters Increment-only counter columns, usually read-only in syncs. Views Curated read-only projections used as sync sources for downstream tools. Counters is specific to Apache Cassandra and Views to Databricks — each maps to any object or custom field on the other side.
Keyspaces Top-level namespaces with replication settings that scope a sync connection. Volumes Unity Catalog file storage used for staging bulk loads. Keyspaces is specific to Apache Cassandra and Volumes to Databricks — each maps to any object or custom field on the other side.
Tables Wide-column tables addressed by partition key, the unit of row-level sync. SQL Warehouses The compute endpoint a sync connects to for query execution. Tables is specific to Apache Cassandra and SQL Warehouses to Databricks — 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. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Partitions and Rows is specific to Apache Cassandra and Change Data Feed to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Apache Cassandra and Databricks

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 Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

Databricks Apache Cassandra Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

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.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Apache Cassandra ⇄ Databricks

Connect Apache Cassandra and Databricks for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Cassandra–Databricks connection.

Real-time

Two-way sync

Changes in Apache Cassandra or Databricks instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Cassandra or Databricks 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 Databricks record.

Observability

Monitoring

Track your Apache Cassandra ⇄ Databricks 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 Databricks.

How the Apache Cassandra and Databricks 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

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
How it works

How to connect Apache Cassandra to Databricks — 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.

  1. 01

    Connect your apps

    Authenticate Apache Cassandra and Databricks with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Apache Cassandra connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Cassandra and Databricks 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Apache Cassandra ⇄ Databricks
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Apache Cassandra Databricks
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Cassandra and Databricks integration FAQ

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.

Learn more about security
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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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 474 integrations available for Apache Cassandra and Databricks.

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