DataPulse Analytics Platform Installation

Prerequisites

System Requirements

Linux/macOS/Windows 10+, 4GB RAM, 20GB storage. ARM64 & x86_64 supported.

Python 3.9+

Required for the CLI and SDK. pip20+ recommended for package management.

Database Access

Read/write access to PostgreSQL 13+, MySQL 8+, or Snowflake account.

API Credentials

Valid DataPulse API key and workspace ID from your admin dashboard.

Installation Methods

Select your preferred installation method. All methods include the core analytics engine, CLI tools, and documentation.

Install via pip

Run the following command in your virtual environment:

pip install datapulse-analytics==2.4.1

Verify Installation

dp-cli --version

Install via npm

Add the Node.js SDK to your project:

npm install @datapulse/sdk@2.4.1

Initialize Project

npx datapulse init

Pull the Docker Image

docker pull datapulse/agent:2.4.1

Run Container

docker run -d --name dp-agent \
  -v $(pwd)/config:/etc/datapulse \
  -e DP_API_KEY=your_api_key \
  datapulse/agent:2.4.1

Download Binary

curl -LO https://releases.datapulse.ai/v2.4.1/datapulse-linux-amd64.tar.gz
tar -xzf datapulse-linux-amd64.tar.gz
sudo mv datapulse /usr/local/bin/

Grant Execution Permissions

chmod +x /usr/local/bin/datapulse

Configuration

After installation, configure your workspace connection and data sources. The CLI will generate a default .datapulse.yaml config file.

# .datapulse.yaml
workspace:
  id: ws_8f29a1b3
  api_key: ${DP_API_KEY}
  region: us-east-1

pipeline:
  name: production-analytics
  schedule: "0 */6 * * *"  # Every 6 hours

sources:
  - type: postgresql
    host: db.example.com
    port: 5432
    schema: public
    ssl: true

Never commit API keys to version control. Use environment variables or a secrets manager.

Verification & Testing

Validate your installation and configuration before running production workloads.

# Run diagnostic check
dp-cli doctor

# Test data source connectivity
dp-cli test-connection --source postgresql

# Execute dry-run of your pipeline
dp-cli run --dry-run --verbose

If all checks pass with status OK, your environment is ready for production deployment.

Troubleshooting

Permission Denied Errors

Ensure your user has read/write access to the config directory and that the API key has the required scopes.

Connection Timeouts

Verify firewall rules allow outbound traffic to api.datapulse.ai:443 and your database port.

Version Mismatch

Run pip install --upgrade datapulse-analytics or update your Docker image tag to match the docs.