Real-time
Two-way sync
Changes in Databricks or Splunk instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Analytics
Databricks to Splunk integration — real-time, two-way sync
Keep Databricks and Splunk 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 Databricks and Splunk
Put the same events, users, and metrics on both sides: Splunk and Databricks stay current in real time, in both directions.
Splunk is where teams explore, visualize, and report; Databricks is the store of record that holds the raw tables and full history behind those views. The two overlap wherever the same events, users, and metrics matter to both, and when the bridge between them is a nightly export or a hand-built extract, dashboards lag the warehouse and analysts spend the morning arguing over whose number is right.
Stacksync syncs Dashboards, Search Results, Saved Searches, Fired Alerts in Splunk with Materialized Views, Volumes, SQL Warehouses, Change Data Feed in Databricks field by field, in real time, and in both directions. You decide which system owns which fields, and Stacksync resolves conflicts by rules you set. Whether the flow is warehouse tables feeding live reports or captured events and segments landing back in Databricks, every copy stays consistent.
Common use cases
- 01 Ingest application, CRM, or database records into Splunk via the HTTP Event Collector so operational data lands alongside logs for correlation and dashboards.
- 02 Read and write KV Store collections to keep Splunk lookups — asset inventories, allow/deny lists, enrichment tables — in sync with an external source of truth in both directions.
- 03 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
Common sync patterns
Where Databricks holds the source tables: live data in the reporting layer
Records maintained in Databricks flow into Splunk as they change, so dashboards and reports read current rows rather than an overnight extract.
Where Splunk produces segments or scores: results back to the warehouse
Cohorts, segments, and computed metrics defined in Splunk write to Databricks as tables the rest of the stack can query and join.
Shared user and account keys
Users and accounts tracked in Splunk line up with the customer or user rows in Databricks on a stable key, so both sides count the same population.
What you can sync between Databricks and Splunk
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 | Splunk objects | How this pairing syncs | |
|---|---|---|---|
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | KV Store Collections App-scoped, MongoDB-backed key-value collections at /servicesNS/{owner}/{app}/storage/collections/data/{collection} with full CRUD and batch endpoints. Genuinely bidirectional lookup/state store — read records out or write records in. | Change Data Feed is specific to Databricks and KV Store Collections to Splunk — each maps to any object or custom field on the other side. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Indexes Index inventory and settings (retention, max size, event counts) via /services/data/indexes, with create and edit; loaded into a database for capacity, retention, and data-onboarding tracking. | Catalogs is specific to Databricks and Indexes to Splunk — each maps to any object or custom field on the other side. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | HTTP Event Collector The write-in path: POST events and metrics to /services/collector (port 8088, or 443 on Splunk Cloud) authenticated with a per-input HEC token, so external records are indexed alongside logs for search and correlation. | Schemas is specific to Databricks and HTTP Event Collector to Splunk — each maps to any object or custom field on the other side. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Users and Roles Accounts at /services/authentication/users and role/capability definitions at /services/authorization/roles, with full CRUD; exported for access reviews or provisioned from an identity source of truth. | Delta Tables is specific to Databricks and Users and Roles to Splunk — each maps to any object or custom field on the other side. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Dashboards Simple XML dashboard and view definitions at /servicesNS/{owner}/{app}/data/ui/views; exported for backup and audit, or created and updated programmatically from version control. | Views is specific to Databricks and Dashboards to Splunk — 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. | Search Results SPL searches dispatched via POST /services/search/jobs return a search ID (SID); results are pulled from /services/search/jobs/{sid}/results once the job completes, or synchronously via oneshot/export mode. The primary read path for streaming indexed events out to a warehouse. | Materialized Views is specific to Databricks and Search Results to Splunk — each maps to any object or custom field on the other side. |
How changes propagate between Databricks and Splunk
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 Splunk 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 Splunk through its API, with automatic retries and rate-limit backoff.
Splunk Databricks Sub-second propagation
DetectionSplunk notifies Stacksync of record changes through webhook events. Time-range searches over indexed events (earliest/latest on _time or _indextime).
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.
- Splunk: The management REST API throttles through search-concurrency quotas — max concurrent searches per user and role, plus per-CPU historical search limits in limits.conf — rather than a fixed per-request rate; exceeding quota queues or blocks jobs. HEC has per-token and per-instance throughput limits, and Splunk Cloud adds ingestion and API limits on top.
What ships with Databricks ⇄ Splunk
Connect Databricks and Splunk for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Splunk connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Splunk 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 Splunk record.
Observability
Monitoring
Track your Databricks ⇄ Splunk 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 Splunk.
How the Databricks and Splunk 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
Splunk
- Integration surface
- REST API (management API + HTTP Event Collector)
- Authentication
- HTTP Basic (username/password), or a session key from POST /services/auth/login sent as Authorization: Splunk <key>, or a bearer authentication token (Authorization: Bearer <token>). The HTTP Event Collector uses its own per-input token (Authorization: Splunk <hec-token>). Management API defaults to port 8089; HEC to port 8088 (443 on Splunk Cloud).
- Change detection
- Time-range searches over indexed events (earliest/latest on _time or _indextime); events are immutable once indexed, so incremental extraction advances a time cursor rather than a modified-date CDC feed. Config objects such as saved searches and KV Store are polled; alerts can push via a saved-search webhook action.
- Capabilities
- read · write · webhooks
- Rate limits
- The management REST API throttles through search-concurrency quotas — max concurrent searches per user and role, plus per-CPU historical search limits in limits.conf — rather than a fixed per-request rate; exceeding quota queues or blocks jobs. HEC has per-token and per-instance throughput limits, and Splunk Cloud adds ingestion and API limits on top.
How it works
How to connect Databricks to Splunk — 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 Splunk 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
Splunk
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Splunk 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 ⇄ SplunkCustomers 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 SplunkCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Splunk integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Splunk: authenticate both systems, choose the objects to sync (such as Databricks's Change Data Feed and Catalogs), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Databricks and Splunk. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Splunk: Time-range searches over indexed events (earliest/latest on _time or _indextime); events are immutable once indexed, so incremental extraction advances a time cursor rather than a modified-date CDC feed. Config objects such as saved searches and KV Store are polled; alerts can push via a saved-search webhook action. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Splunk side: Dashboards, Search Results, Saved Searches, Fired Alerts, plus custom fields where Splunk exposes them. On the Databricks side: Materialized Views, Volumes, SQL Warehouses, Change Data Feed. 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 Databricks and Splunk: Where Databricks holds the source tables: live data in the reporting layer; Where Splunk produces segments or scores: results back to the warehouse; Shared user and account keys. Records maintained in Databricks flow into Splunk as they change, so dashboards and reports read current rows rather than an overnight extract.
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 522 integrations available for Databricks and Splunk.
Popular · 7 of 522
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Databricks ⇄ Mixpanel -
Databricks ⇄ Microsoft Power Bi -
Databricks ⇄ Tableau -
Databricks ⇄ Adobeanalytics -
Splunk ⇄ MotherDuck -
Splunk ⇄ AWS S3 -
Splunk ⇄ Amazon Redshift
No integrations match .
