Please use the form below to provide your feedback. Because your feedback is valuable to us, the information you submit in this form is recorded in our issue tracking system (JIRA), which is publicly available. You can track the status of your feedback using the ticket number displayed in the dialog once you submit the form.

Do a Quick Install

      +
      First-time users can get Enterprise Analytics running simply and rapidly by using Docker. Once you install Docker, you can use a single command to download and install Enterprise Analytics on your computer.

      Install Enterprise Analytics using Docker and S3Mock

      This guide walks you through running Couchbase Enterprise Analytics in Docker and configuring it to use Adobe S3Mock as its blob storage backend — ideal for local development and testing without relying on real AWS S3 infrastructure or appliance.

      Prerequisites:

      • Docker Desktop installed (macOS, Windows, or Linux)

      • No other services using ports 8091 or 8095

      • No existing Docker containers named s3mock or ea

      • No existing Docker network named ea

      Step-by-Step Setup

      1. Create a Docker Network

      docker network create ea

      This command creates the ea network, enabling communication between the Enterprise Analytics and S3Mock containers.

      2. Create the S3Mock Container

      We use S3Mock to emulate AWS S3 behavior locally.

      docker run -d --name s3mock --network ea \
  -e initialBuckets=cloud-storage-container \
  -e root=fs \
  -e retainFilesOnExit=true \
  adobe/s3mock

      This starts the S3Mock container with a pre-created persistent bucket named cloud-storage-container for Enterprise Analytics to uses, configured to communicate over the ea network.

      3. Create the Couchbase Enterprise Analytics Container

      docker run -d --name ea --network ea \
  -p 8091:8091 -p 8095:8095 \
  couchbase/enterprise-analytics:2.1.0

      This exposes the Enterprise Analytics UI and REST APIs on ports 8091 and 8095 respectively.

      4. Configure Blob Storage Settings

      Once Enterprise Analytics is online, configure it to use the local S3Mock container as its blob storage:

      curl -X POST http://localhost:8091/settings/analytics \
  -d blobStorageScheme=s3 \
  -d blobStorageBucket=cloud-storage-container \
  -d blobStorageRegion=us-east-1 \
  -d blobStorageEndpoint=http://s3mock:9090 \
  -d blobStorageAnonymousAuth=true \
  -d blobStoragePathStyleAddressing=true \
  -d numStoragePartitions=16

      Explanation of settings:

      • blobStorageScheme: Use S3 (S3Mock is S3-compatible)

      • blobStorageBucket: Bucket name created in S3Mock

      • blobStorageRegion: Region name (arbitrary for S3Mock)

      • blobStorageEndpoint: The local S3Mock container’s URL

      • blobStorageAnonymousAuth=true: Required, since S3Mock does not use IAM

      • blobStoragePathStyleAddressing=true: Required, as S3Mock does not support virtual-host-style addressing, which is the default for AWS S3

      • numStoragePartitions: Number of storage partitions (the default of 128 adds unnecessary overhead for a local quick start cluster)

      5. Initialize the Enterprise Analytics Cluster

      Finally, complete the standalone initialization of the Enterprise Analytics cluster:

      curl -X POST http://localhost:8091/clusterInit \
  -d username=Administrator \
  -d password=password \
  -d port=SAME \
  -d memoryQuota=100 \
  -d clusterName="EA Quick Start Cluster"

      You can now:

      • Log in at http://localhost:8091 using Administrator/password

      • Install the travel-sample dataset

      • Use the Workbench in the UI to run SQL++ queries, etc.

      • Provision additional nodes (e.g. docker containers) as desired, by repeating step 3 above, selecting unique container names and bind ports from the ea container, and joining them to the cluster using the UI or REST API. See Add a Node and Rebalance for details.

      Next Steps

      Other Destinations

      • Create a Cluster: Provides a detailed explanation of how to provision a Enterprise Analytics-node, and thereby create Enterprise Analytics cluster. This is the procedure you’ll certainly use in production as well as for testing different configurations. The available options include use of the Enterprise Analytics Web Console, the Couchbase REST API, and the Couchbase Command Line Interface.

      • Start and Stop Enterprise Analytics: Explains how to start and stop the service and application using the commands that are specific to your underlying platform.