Real-time
Two-way sync
Changes in Databricks or Redis Enterprise instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Databricks to Redis Enterprise integration — real-time, two-way sync
Keep Databricks and Redis Enterprise 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 Databricks and Redis Enterprise
Connect Redis Enterprise 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 Redis Enterprise'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 Redis Enterprise where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Redis Enterprise sync into Databricks in real time, and result tables in Databricks sync back into Redis Enterprise, 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 Sync Postgres reference tables into RedisJSON documents that power API responses and personalization lookups.
- 04 Publish record-change events into Redis Streams so microservices react to upstream CRM updates without polling.
Common sync patterns
Operational data in the warehouse, minus the pipeline
Rows from Redis Enterprise land in Databricks as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Databricks sync into Redis Enterprise, where whatever reads from that database gets them without querying the warehouse.
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
What you can sync between Databricks and Redis Enterprise
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.
| Databricks objects | Redis Enterprise objects | How this pairing syncs | |
|---|---|---|---|
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Pub/Sub channels Fire-and-forget messaging used to notify applications when synced keys change. | Delta Tables is specific to Databricks and Pub/Sub channels to Redis Enterprise — each maps to any object or custom field on the other side. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Search indexes Secondary indexes (RediSearch) that make synced hashes and JSON documents queryable. | Views is specific to Databricks and Search indexes to Redis Enterprise — each maps to any object or custom field on the other side. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Keys (Strings) Simple key-value pairs used to cache individual synced records or lookup values. | Materialized Views is specific to Databricks and Keys (Strings) to Redis Enterprise — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Hashes Field-value maps that commonly hold one synced row per hash, keyed by record ID. | Volumes is specific to Databricks and Hashes to Redis Enterprise — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | JSON documents Native JSON storage (RedisJSON) for nested records synced from APIs or document stores. | SQL Warehouses is specific to Databricks and JSON documents to Redis Enterprise — each maps to any object or custom field on the other side. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Sets Unordered unique-member collections used for membership checks like segment or ID lists. | Change Data Feed is specific to Databricks and Sets to Redis Enterprise — each maps to any object or custom field on the other side. |
How changes propagate between Databricks and Redis Enterprise
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.
Databricks Redis Enterprise 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 applied to Redis Enterprise as a row-level write, with types converted between the two schemas.
Redis Enterprise Databricks Interval-based propagation
DetectionStacksync polls Redis Enterprise for changes on an incremental schedule, reading only records changed since the previous pass. Keyspace notifications over pub/sub or reads from Redis Streams.
DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
- Redis Enterprise: Throughput is bounded by provisioned cluster capacity rather than published API rate limits.
What ships with Databricks ⇄ Redis Enterprise
Connect Databricks and Redis Enterprise for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Redis Enterprise connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Redis Enterprise 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 Databricks or Redis Enterprise record.
Observability
Monitoring
Track your Databricks ⇄ Redis Enterprise sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Databricks and Redis Enterprise.
How the Databricks and Redis Enterprise connectors work
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
Redis Enterprise
- Integration surface
- Redis wire protocol (RESP) via client libraries; separate REST API for cluster management
- Authentication
- Password or ACL-based credentials, typically over TLS
- Change detection
- Keyspace notifications over pub/sub or reads from Redis Streams; no transaction-log CDC surface for data
- Capabilities
- read · write
- Rate limits
- Throughput is bounded by provisioned cluster capacity rather than published API rate limits
How it works
How to connect Databricks to Redis Enterprise — 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 Databricks and Redis Enterprise with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Databricks
connected
Redis Enterprise
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Redis Enterprise 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 · Databricks ⇄ Redis EnterpriseCustomers 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.
Databricks Redis EnterpriseCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Redis Enterprise integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Redis Enterprise: authenticate both systems, choose the objects to sync (such as Databricks's Delta Tables and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Databricks side: Change Data Feed, Catalogs, Schemas, Delta Tables, plus custom fields where Databricks exposes them. On the Redis Enterprise side: Sets, Sorted Sets, Lists, Streams. 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.
-
Common patterns for Databricks and Redis Enterprise: Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed; Fresh analytics without loading windows. Rows from Redis Enterprise land in Databricks as they change, replacing hand-built CDC and batch extract jobs.
-
Databricks: 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. Redis Enterprise: Redis wire protocol (RESP) via client libraries; separate REST API for cluster management. Authentication: Password or ACL-based credentials, typically over TLS. Stacksync manages authentication, retries, and rate limits on both sides.
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Databricks: Delta Lake's Change Data Feed records row-level inserts, updates, and deletes, enabling incremental sync without full scans. Redis Enterprise: Data structures are typed server-side (hashes, sets, sorted sets, streams), so sync mappings target a structure and key convention rather than tables and columns. Stacksync's field mapping accounts for these differences between Databricks and Redis Enterprise 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
CSA STAR
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 392 integrations available for Databricks and Redis Enterprise.
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Databricks ⇄ YugabyteDB -
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Redis Enterprise ⇄ Apache Doris -
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Redis Enterprise ⇄ Apache Impala
No integrations match .
