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Elastic Cloud Integration Guide

Elastic Cloud integrates with enterprise systems primarily through deployment-specific Elasticsearch REST APIs, bulk operations, authenticated queries, and selected Kibana webhook-style notifications.

Elastic Cloud integration options at a glance

Elastic Cloud integrations primarily use the Elasticsearch REST API associated with a specific deployment. These APIs support document indexing and retrieval, searches, aggregations, index and alias management, ingest pipelines, bulk operations, asynchronous searches, and task monitoring. Elastic Cloud management APIs address deployment and organization administration separately. Kibana rules and connectors can provide webhook-style notifications for selected conditions, but not guaranteed callbacks for every document change. API keys, service tokens, bearer authentication, and basic authentication are available depending on the endpoint. Martini can authenticate securely, orchestrate scheduled or event-triggered workflows, transform payloads, use bounded bulk writes, expose a controlled search API, and route failures for retry or remediation.

Integration pointSupported by Elastic Cloud?Common use casesHow Martini supports it
Elasticsearch REST APIsYesIndex, retrieve, update, and delete Documents; search and aggregate data; manage Indices, Aliases, mappings, templates, Data streams, and Ingest pipelines.Martini can consume deployment-specific REST endpoints, map request and response payloads, apply business rules, and orchestrate multi-step workflows.
Bulk, asynchronous, and batch APIsYesThe _bulk API supports batched index, update, and delete operations. Asynchronous search, reindex, update-by-query, scroll, point-in-time, and task APIs support larger or longer-running operations.Martini can construct bounded batches, inspect item-level outcomes, monitor asynchronous tasks, and retry only appropriate failures.
Webhooks and outbound callbacksLimitedKibana rules and connectors can issue webhook-style HTTP notifications for selected rule conditions; this is not a universal callback for document mutations.Martini can expose an endpoint to receive selected Kibana notifications, validate secrets or signatures where configured, enrich events, and route them downstream.
Database and analytics accessYesThe Elasticsearch SQL API provides SQL-oriented access, while Query DSL, aggregations, and ES|QL support search and analytics scenarios subject to deployment version and feature availability.Martini can call supported query endpoints, validate business parameters, transform results, and expose a controlled data service.
AuthenticationYesElasticsearch supports API keys, basic authentication, service account tokens, bearer authentication, and privilege-based authorization across relevant endpoints.Martini can keep deployment endpoints and credentials in protected configuration and send least-privilege authentication with HTTPS certificate validation.
Elastic Cloud management APIsYesSeparate management APIs support deployment and organization-level administration rather than ordinary document indexing and search.Martini can call management endpoints when required, while keeping their permissions and endpoint configuration separate from Elasticsearch data APIs.
File and attachment handlingNot confirmedElastic Cloud is primarily designed for JSON Documents, not as a general-purpose file or attachment repository.Martini can transform source files into JSON metadata or documents and preserve references to external file storage where appropriate.
GraphQL APIsNot confirmedGraphQL is not the primary documented Elastic Cloud integration interface. An external or Martini API can abstract Elasticsearch queries if required.Martini can expose a controlled REST API façade; GraphQL should not be represented as a native Elastic Cloud interface.

How Elastic Cloud exposes data and business events

Elastic Cloud REST APIs

Elasticsearch REST APIs are the principal integration surface for a deployment. They support document operations, searches, aggregations, index and alias management, templates, ingest pipelines, health checks, and administrative tasks. Elastic Cloud management APIs are separate from the Elasticsearch endpoint used for data operations.

Martini implementation pattern

Martini implementation pattern: a workflow receives a source payload or request, selects the deployment endpoint and target object from protected configuration, sends an authenticated REST request, validates the response, and transforms the result for the next system or API consumer.

Implementation sequence

Receive a source payload, API request, or scheduled trigger
Select the deployment endpoint and target index, data stream, or alias
Authenticate with a least-privilege API key or service identity
Map and validate the request against the Elasticsearch operation
Send the REST request and inspect the response
Persist results, checkpoints, or remediation details

Bulk and asynchronous APIs

The _bulk API supports batched index, update, and delete operations. Asynchronous search, reindex, update-by-query, scroll, point-in-time, and task APIs support controlled traversal or long-running work.

Martini implementation pattern

Martini implementation pattern: the workflow groups bounded payloads, submits the appropriate bulk or asynchronous operation, parses overall and item-level results, monitors tasks where necessary, and retries only eligible failures.

Implementation sequence

Collect and validate a bounded batch of source items
Assign stable document IDs and operation types
Submit the _bulk or asynchronous request
Parse each item result rather than relying only on HTTP status
Separate retryable failures from permanent mapping or validation errors
Monitor tasks and store completion or remediation state

Kibana webhook-style notifications

Kibana rules and connectors can invoke HTTP webhook-style actions for selected conditions. These notifications are rule-driven and do not guarantee a callback for every Elasticsearch document creation, update, or deletion.

Martini implementation pattern

Martini implementation pattern: a Martini API receives the selected notification, validates its source and payload, enriches it with relevant context, applies routing and deduplication rules, and invokes the downstream workflow or application.

Implementation sequence

Receive the selected Kibana notification
Validate the request and configured secret or signature
Normalize the rule payload and identify the alert
Check duplicate or acknowledgement state
Enrich and route the notification to the target system
Return an appropriate response and record failures for retry

Elasticsearch SQL and analytics APIs

Elasticsearch provides SQL-oriented access through its SQL API and additional query and analytics capabilities through Query DSL, aggregations, and ES|QL features where available for the target deployment and version.

Martini implementation pattern

Martini implementation pattern: a controlled Martini API accepts business-level search or reporting parameters, validates allowed fields and limits, translates them into a supported query, calls Elastic Cloud, and maps the response into a stable consumer-facing model.

Implementation sequence

Receive a validated business search or analytics request
Apply authorization, field restrictions, and result-size limits
Translate approved parameters into SQL, Query DSL, or supported analytics syntax
Call the target Elasticsearch endpoint
Transform hits, aggregations, or tabular results
Return a controlled response and log correlation metadata

Common Elastic Cloud integration patterns

Pattern 1: Index application data in Elastic Cloud

When to use this pattern

Use this pattern when customer, order, product, case, or operational data must be searchable in Elastic Cloud. The workflow should normalize the source model, apply mapping and validation rules, and preserve a stable relationship between the source object and its Elasticsearch Document.

Integration direction
Source application
Martini
Elastic Cloud
Example Mapping
Elastic Cloud FieldCanonical FieldTarget Field
source.idbusinessObjectId_id
source.updatedAtlastModifiedAtupdated_at
source.statusbusinessStatusstatus
source.attributessearchableAttributesattributes
Martini implementation pattern

Martini receives or retrieves the source payload, maps it into the target Document, applies required-field and status rules, selects an Index, Data stream, or Alias from environment configuration, and writes the result through the REST or _bulk API. The workflow records item-level failures and retries only transient or capacity-related errors.

Martini capabilities used
  • workflows
  • API consumption
  • data mapping
  • business rules
  • bulk orchestration
  • error handling

Pattern 2: Run scheduled incremental synchronization

When to use this pattern

Use this pattern when a source application exposes changed-since queries but does not provide reliable document-level callbacks. A scheduler-driven workflow can maintain a checkpoint, account for late-arriving updates, and make repeatable writes to Elastic Cloud.

Integration direction
Source system
Martini
Elastic Cloud
Example Mapping
Elastic Cloud FieldCanonical FieldTarget Field
source.modifiedAtchangeTimestampupdated_at
source.externalKeystableDocumentId_id
source.typedocumentTypedocument_type
source.payloaddocumentBody_source
Martini implementation pattern

A scheduled Martini workflow reads the last successful checkpoint, queries the source for an incremental window, maps and enriches each item, and submits bounded bulk operations. It advances the checkpoint only after successful processing, uses overlap windows for late updates, and avoids duplicates with deterministic document IDs.

Martini capabilities used
  • scheduled workflows
  • checkpoint orchestration
  • data mapping
  • transformations
  • bulk APIs
  • retry handling

Pattern 3: Expose a controlled search API façade

When to use this pattern

Use this pattern when downstream applications need business-friendly search without direct access to Elasticsearch Query DSL. The façade can enforce authorization, allowed fields, pagination limits, and a stable response contract.

Integration direction
Client application
Martini
Elastic Cloud
Martini response
Example Mapping
Elastic Cloud FieldCanonical FieldTarget Field
request.customerIdcustomerIdentifierterm query
request.queryapprovedSearchTextmulti-field search
request.pageTokenpaginationCursorsearch_after
response.hitsmatchedItemsitems
Martini implementation pattern

Martini exposes a REST API, validates the request, applies authorization and result-size rules, translates approved parameters into Elasticsearch queries, and calls the deployment REST API. It maps hits and aggregations to a controlled response and rejects arbitrary query structures that could expose data or overload the deployment.

Martini capabilities used
  • API exposure
  • workflow orchestration
  • request validation
  • data mapping
  • business rules
  • security controls

Pattern 4: Route Kibana alert notifications

When to use this pattern

Use this pattern when Kibana rules identify selected conditions and downstream systems need an operational response. It is appropriate for rule-driven notifications, not guaranteed synchronization of every document mutation.

Integration direction
Kibana
Martini
ServiceNow or other downstream application
Example Mapping
Elastic Cloud FieldCanonical FieldTarget Field
rule.namealertNameshort_description
alert.severityprioritypriority
alert.timestampoccurredAtopened_at
alert.contextdiagnosticContextdescription
Martini implementation pattern

Kibana invokes a Martini endpoint through a webhook-style action. Martini authenticates and validates the notification, checks an alert identifier for duplicates, enriches the event, applies severity-based routing, and sends it to the downstream application. Retry and acknowledgement state are recorded separately from the initial notification receipt.

Martini capabilities used
  • API exposure
  • webhook consumption
  • validation
  • enrichment
  • conditional routing
  • error handling

Applications commonly integrated with Elastic Cloud

Elastic Cloud commonly sits within Elastic Stack ingestion and observability architectures and can also serve as a search and analytics target for enterprise applications. Martini can mediate these flows when data needs transformation, business rules, secure API exposure, checkpointing, or controlled error handling.

Application Scenario Direction Martini Pattern
Logstash Logstash can ingest, parse, enrich, and route operational or application events into Elasticsearch. Logstash → Martini → Elastic Cloud Martini can receive or retrieve normalized Logstash output, apply validation and enrichment rules, and write documents or batches to the deployment’s indices, data streams, or aliases. Bulk item failures are separated for retry or remediation.
Beats Beats can ship logs, metrics, uptime data, and host telemetry into Elastic infrastructure for search and observability. Beats → Elastic Cloud Where Martini is used in the flow, it can mediate incoming telemetry, normalize fields, route data to the appropriate data stream or index, and apply bounded batching and operational monitoring.
Elastic APM Server Elastic APM Server sends application performance, transaction, trace, and error telemetry to Elasticsearch. Elastic APM Server → Elastic Cloud Martini can participate as an orchestration or governance layer around APM-related data flows, applying deployment-specific authentication, routing, validation, and operational handling before Elasticsearch writes where the architecture requires it.
Kibana Kibana searches, visualizes, administers, and evaluates alerting rules against data stored in Elasticsearch. Kibana → Martini → Downstream applications Kibana rules can call a Martini endpoint through a webhook-style action. Martini validates the notification, enriches it, applies routing rules, and forwards it to a downstream application while recording duplicate and retry handling.
Apache Kafka Apache Kafka can distribute application or event data for ingestion into Elastic Cloud and support event-streaming architectures around indexed data. Apache Kafka → Martini → Elastic Cloud Martini can consume or orchestrate event payloads where the surrounding deployment exposes a supported endpoint, map event fields to Elasticsearch documents, and use bounded bulk writes with item-level failure processing.
Salesforce Salesforce data such as Accounts, Contacts, and Cases can be indexed for unified search and operational analytics. Salesforce → Martini → Elastic Cloud A Martini workflow retrieves changed Salesforce objects, maps them to a deliberate Elasticsearch document model, applies stable document IDs and business rules, and writes them through the Elasticsearch REST and bulk APIs.
ServiceNow ServiceNow incidents, requests, configuration data, and knowledge content can be indexed for search, reporting, and operational workflows. ServiceNow → Martini → Elastic Cloud Martini can retrieve incremental ServiceNow data, normalize fields and timestamps, index documents using deterministic IDs, and route Elastic Cloud alert notifications back to ServiceNow when required.
Jira Jira issues, projects, comments, and statuses can be indexed for cross-system search and operational reporting. Jira → Martini → Elastic Cloud Martini can call Jira APIs on a schedule or in response to an upstream trigger, transform issue data into the target index mapping, use bulk operations, and isolate mapping or authorization failures from retryable transport errors.

How to build a Elastic Cloud integration in Martini

Objective

Configure the Elastic Cloud deployment endpoint and authentication without embedding credentials in workflow logic.

Instructions in Martini

  • Use the deployment-specific Elasticsearch endpoint for data operations.
  • Prefer a least-privilege API key or service identity for programmatic access.
  • Store endpoints, keys, and related settings in protected environment configuration.
  • Keep Elastic Cloud management API permissions separate from Elasticsearch data permissions.

Objective

Select a trigger that matches the synchronization requirement and the reliability of the available vendor event mechanism.

Instructions in Martini

  • Use an API request or source event for interactive integrations.
  • Use a scheduler for incremental synchronization with a checkpoint.
  • Receive Kibana webhook-style notifications only for selected rule conditions.
  • Use an event-streaming architecture when polling is unsuitable for high-volume or low-latency flows.

Objective

Obtain source data or notification context before constructing the Elastic Cloud operation.

Instructions in Martini

  • Retrieve changed source objects using a bounded incremental query.
  • Receive and validate selected Kibana notifications at a Martini API.
  • Use search_after, point-in-time, or applicable scroll patterns for large result sets.
  • Capture correlation identifiers and source timestamps for traceability.

Objective

Coordinate retrieval, transformation, Elasticsearch calls, branching, and state updates in one maintainable Martini workflow.

Instructions in Martini

  • Separate source retrieval, transformation, write, and checkpoint stages.
  • Select the target Index, Data stream, or Alias from controlled configuration.
  • Branch permanent validation failures from retryable transport or capacity errors.
  • Use asynchronous task monitoring for long-running operations where required.

Objective

Create a deliberate Elasticsearch document model that is compatible with mappings, analyzers, timestamps, and search requirements.

Instructions in Martini

  • Map source fields to stable Document properties.
  • Assign deterministic IDs from source business keys.
  • Normalize dates, nested structures, statuses, and searchable text.
  • Validate required fields and reject incompatible mapping changes before writing.

Objective

Enforce authorization, routing, idempotency, and data-quality rules before calling Elastic Cloud.

Instructions in Martini

  • Limit search fields, result sizes, and permitted query parameters.
  • Choose an Index, Data stream, or Alias according to mutability and retention requirements.
  • Avoid duplicate writes by repeating stable document operations on retry.
  • Apply severity, tenant, or document-type routing rules where applicable.

Common Elastic Cloud data objects used in integrations

ObjectTypical UseCommon target systemsMartini handling
DocumentsJSON objects representing application, operational, log, metric, or business data indexed and retrieved through Elasticsearch.Salesforce, ServiceNow, Jira, applications, data lakes, and reporting servicesMartini maps source payloads to the document model, applies validation and enrichment, assigns deterministic IDs, and handles index or bulk responses.
IndicesCollections of Documents with mappings and settings for search, updates, retention, and analysis.Source applications, search APIs, reporting applications, and migration workflowsMartini selects index names from protected configuration, validates routing rules, and can orchestrate index, reindex, alias, and migration operations.
Data streamsTime-series-oriented logical collections backed by rollover indices for logs, metrics, and traces.Beats, Logstash, APM Server, observability applications, and analytics workflowsMartini routes append-oriented events to the correct data stream and avoids treating data streams as mutable customer or order stores without an explicit design.
AliasesNamed references to one or more indices, commonly used for read/write abstraction and zero-downtime index changes.Applications, search façades, migration workflows, and reindex processesMartini can resolve configured aliases, write through approved endpoints, and coordinate controlled cutovers after validation.
Index templatesReusable settings, mappings, and component-template combinations applied to new indices or data streams.Deployment administration, ingestion workflows, and index lifecycle processesMartini can apply versioned templates through REST workflows and validate deployment responses before enabling new writes.
Ingest pipelinesPre-index processing definitions that transform, enrich, or validate Documents before storage.Ingestion applications, Logstash, Beats, APM Server, and operational data flowsMartini can select a configured pipeline, send documents through the appropriate endpoint, and distinguish pipeline or mapping errors from transient failures.

Authentication and security considerations

Use least-privilege authentication

Elastic Cloud supports API keys, basic authentication, service account tokens, bearer authentication, and privilege-based authorization for relevant endpoints. A dedicated API key with only the required cluster and index privileges is generally the preferred programmatic pattern.

Protect endpoints and secrets

Use HTTPS with certificate validation enabled. Store deployment endpoints, API keys, and service credentials in Martini protected configuration or secrets rather than embedding them in workflows. Keep Elastic Cloud management permissions separate from data access permissions.

Control exposed search access

When Martini exposes a search API façade, validate query parameters, restrict fields and result sizes, apply authorization rules, and avoid exposing arbitrary Elasticsearch Query DSL to untrusted clients.

Operational considerations for Elastic Cloud integrations

Capacity, concurrency, and retries

Elastic Cloud capacity depends on deployment size, node roles, storage, shard layout, query complexity, and concurrent indexing. Martini workflows should bound concurrency and batch sizes, use backoff, and avoid unbounded retries.

Bulk responses and idempotency

A successful HTTP response from _bulk does not mean every item succeeded. Inspect item-level results, retry only eligible failures, and use stable source identifiers as Document IDs so repeated operations remain idempotent.

Pagination and consistency

Use search_after, point-in-time searches, or applicable scroll patterns for large result sets rather than repeatedly increasing from. Define refresh expectations when a workflow writes and then immediately searches for the same data.

Mappings and version compatibility

Field types, analyzers, nested structures, date formats, and dynamic mappings should be designed deliberately. Verify query syntax, ES|QL availability, and API behavior against the target deployment version. Use aliases to support controlled index migrations when mappings must change.

Observability and testing

Capture correlation identifiers, deployment and index names, operation types, batch sizes, latency, and item-level failure counts without logging credentials or sensitive document content. Test mapping changes, partial bulk failures, authorization errors, and capacity responses before production rollout.

Why use Martini instead of scripts or point-to-point integrations?

Orchestrate more than an API call

Point-to-point scripts often combine authentication, pagination, transformation, retries, and business rules in code that is difficult to reuse. Martini provides workflows and APIs for coordinating these concerns around Elastic Cloud’s native REST interfaces.

Make mappings and rules maintainable

Martini separates data mapping, validation, routing, checkpointing, and error handling from vendor-specific request details. This helps teams reuse integration assets when multiple applications publish data to different indices or data streams.

Support controlled API exposure

Martini can expose a business-oriented search API that validates requests and translates approved parameters into Elasticsearch queries, reducing the need for every consumer to understand deployment-specific Query DSL or security boundaries.

Improve operational reliability

Workflows can use bounded batches, item-level bulk error handling, scheduled checkpoints, protected configuration, and monitoring practices that are difficult to standardize across isolated scripts.

Frequently asked questions

How can Elastic Cloud be integrated with enterprise systems?

Elastic Cloud is primarily integrated through the Elasticsearch REST APIs of a specific deployment. These APIs support document operations, searches, aggregations, index and alias management, ingest pipelines, bulk processing, asynchronous searches, and task monitoring. Kibana can also issue webhook-style notifications for selected rule conditions, while separate Elastic Cloud management APIs support deployment and organization administration.

Can Martini integrate with Elastic Cloud?

Yes. Martini can consume Elastic Cloud’s Elasticsearch REST APIs, use authenticated bulk and search operations, orchestrate scheduled synchronization, expose a controlled search API, and receive selected Kibana webhook-style notifications. No native Martini Elastic Cloud connector is documented in the supplied sources.

Do I need a connector to integrate Elastic Cloud with Martini?

No. A dedicated Elastic Cloud connector is not required. Martini can integrate using Elastic Cloud’s confirmed native mechanisms, including Elasticsearch REST APIs, bulk APIs, supported authentication methods, and selected Kibana HTTP notification endpoints.

Is there any extra Lonti cost to integrate Elastic Cloud with Martini?

Lonti does not charge an additional per-connector or per-vendor fee to integrate Elastic Cloud. Integrations are subject to the provisioned capacity of the Martini environment. Separate costs may apply from Elastic, cloud infrastructure, or other third-party systems depending on subscription, usage, and deployment model.

Which Elastic Cloud APIs should an integration use?

Use the Elasticsearch API for Documents, Indices, Data streams, Aliases, searches, mappings, templates, ingest pipelines, and bulk operations. Use Elastic Cloud management APIs for deployment and organization administration. These are separate API surfaces and may require different endpoints and permissions.

Are Elastic Cloud webhooks or events available for synchronization?

Kibana rules and connectors can issue webhook-style notifications for selected conditions, but Elastic Cloud does not provide a general-purpose callback for every document creation, update, or deletion. Reliable synchronization should use scheduled checkpoints, suitable change-data-capture capabilities, or an intermediary event pipeline rather than assuming universal document webhooks.

How does Martini synchronize and transform Elastic Cloud data?

Martini can retrieve changed source data on a schedule or receive selected notifications, map it to a deliberate Elasticsearch document model, normalize timestamps and nested data, apply business rules, and write through the REST or _bulk API. Stable document IDs and checkpoints support repeatable, incremental synchronization.

How are errors, retries, and duplicate documents handled?

Martini can distinguish authentication, mapping, query, validation, capacity, timeout, and transport failures. For bulk requests it should inspect each item result, retry only eligible failures with bounded backoff, and route permanent failures for remediation. Deterministic document IDs make retries idempotent and reduce duplicate Documents.

Related Martini documentation

APIs

Workflows

Data

Security

Operations

Integrate Elastic Cloud with Martini

Use Martini to connect enterprise applications to Elastic Cloud through authenticated Elasticsearch APIs, workflow orchestration, controlled API façades, transformation, and reliable bulk synchronization.

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