Real-time
Two-way sync
Changes in Apache Pinot or Splunk instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Analytics
Apache Pinot to Splunk integration — real-time, two-way sync
Keep Apache Pinot 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 Apache Pinot and Splunk
Put the same events, users, and metrics on both sides: Splunk and Apache Pinot stay current in real time, in both directions.
Splunk is where teams explore, visualize, and report; Apache Pinot 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 Search Results, Saved Searches, Fired Alerts, KV Store Collections in Splunk with Segments, Real-time Tables, Offline Tables, Indexes in Apache Pinot 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 Apache Pinot, every copy stays consistent.
Common use cases
- 01 Export Users and Roles into an IAM database for periodic access reviews, or provision them from an identity source of truth.
- 02 Sync fired-alert history and saved-search definitions into Postgres so security and reliability teams query alert trends and detection coverage in SQL.
- 03 Sync Pinot aggregates into a warehouse to join low-latency metrics with modeled historical data.
- 04 Serve user-facing analytics from Pinot while syncing daily rollups to finance and ops tools.
Common sync patterns
One number both sides agree on
Metrics and aggregates stay aligned between the two systems, so a figure shown in Splunk matches the Apache Pinot table it was built from instead of drifting between refreshes.
Where Apache Pinot holds the source tables: live data in the reporting layer
Records maintained in Apache Pinot 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 Apache Pinot as tables the rest of the stack can query and join.
What you can sync between Apache Pinot 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.
| Apache Pinot objects | Splunk objects | How this pairing syncs | |
|---|---|---|---|
| Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. | 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. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Offline Tables Batch-loaded tables merged with real-time data at query time. | Saved Searches Scheduled searches, reports, and the definitions behind alerts at /services/saved/searches with full create, update, and delete. Read out for governance and coverage review, or provisioned and updated from a config source. | Offline Tables is specific to Apache Pinot and Saved Searches to Splunk — each maps to any object or custom field on the other side. | |
| Tenants Logical groupings that isolate workloads on shared clusters. | Fired Alerts Triggered alert instances listed at /services/alerts/fired_alerts; alert configuration (conditions, schedule, actions) lives on the corresponding saved search. Landed in a database for alert-trend and detection-coverage reporting. | Tenants is specific to Apache Pinot and Fired Alerts to Splunk — each maps to any object or custom field on the other side. | |
| Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. | 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. | Tables is specific to Apache Pinot and KV Store Collections to Splunk — each maps to any object or custom field on the other side. | |
| Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. | 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 Apache Pinot and HTTP Event Collector to Splunk — each maps to any object or custom field on the other side. | |
| Segments Immutable data files that batch ingestion uploads and the cluster serves. | 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. | Segments is specific to Apache Pinot and Users and Roles to Splunk — each maps to any object or custom field on the other side. |
How changes propagate between Apache Pinot 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.
Apache Pinot Splunk Interval-based propagation
DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.
DeliveryEach detected change is written to Splunk through its API, with automatic retries and rate-limit backoff.
Splunk Apache Pinot 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 Apache Pinot as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
- 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 Apache Pinot ⇄ Splunk
Connect Apache Pinot and Splunk for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Pinot–Splunk connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Pinot 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 Apache Pinot or Splunk record.
Observability
Monitoring
Track your Apache Pinot ⇄ Splunk sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Apache Pinot and Splunk.
How the Apache Pinot and Splunk connectors work
Apache Pinot
- Integration surface
- REST API (SQL queries via the broker; administration via the controller); JDBC client available
- Authentication
- Deployment-dependent: HTTP basic authentication or token-based auth where enabled
- Change detection
- Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
- Capabilities
- read · write
- Rate limits
- No fixed API quotas; query throughput depends on broker and server sizing
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 Apache Pinot 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 Apache Pinot 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.
Apache Pinot
connected
Splunk
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Pinot 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 · Apache Pinot ⇄ 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.
Apache Pinot SplunkCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Pinot and Splunk integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Pinot and Splunk: authenticate both systems, choose the objects to sync (such as Apache Pinot's Indexes and Offline Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Change detection on Apache Pinot: Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes. 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: Search Results, Saved Searches, Fired Alerts, KV Store Collections, plus custom fields where Splunk exposes them. On the Apache Pinot side: Segments, Real-time Tables, Offline Tables, Indexes. 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 Apache Pinot and Splunk: One number both sides agree on; Where Apache Pinot holds the source tables: live data in the reporting layer; Where Splunk produces segments or scores: results back to the warehouse. Metrics and aggregates stay aligned between the two systems, so a figure shown in Splunk matches the Apache Pinot table it was built from instead of drifting between refreshes.
-
Apache Pinot: REST API (SQL queries via the broker; administration via the controller); JDBC client available. Authentication: Deployment-dependent: HTTP basic authentication or token-based auth where enabled. Splunk: 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). Stacksync manages authentication, retries, and rate limits on both sides.
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
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