Two-way sync
Changes in Databricks or Redis Enterprise instantly reflect in both systems. No stale data, no manual imports.
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.
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.
Rows from Redis Enterprise land in Databricks as they change, replacing hand-built CDC and batch extract jobs.
Aggregates or model outputs computed in Databricks sync into Redis Enterprise, where whatever reads from that database gets them without querying the warehouse.
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
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 | |
|---|---|---|
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Redis Enterprise connection.
Changes in Databricks or Redis Enterprise instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Databricks or Redis Enterprise data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Databricks or Redis Enterprise record.
Track your Databricks ⇄ Redis Enterprise sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Databricks and Redis Enterprise.
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.
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.
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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
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.
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.
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.
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.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Databricks and Redis Enterprise.