Quickstart Guide

Get your analytics pipeline running in under 5 minutes. This guide walks you through initializing the DataPulse SDK, configuring your first data stream, and validating event ingestion.

1. Install the SDK

DataPulse provides official SDKs for JavaScript, Python, and Go. We'll use the npm package for this guide:

npm install @datapulse/analytics-sdk
# or with yarn
yarn add @datapulse/analytics-sdk

2. Initialize & Configure

Import the client and initialize it with your project credentials. Securely store your keys using environment variables.

import { DataPulseClient } from '@datapulse/analytics-sdk';

const client = new DataPulseClient({
  apiKey: process.env.DATAPULSE_API_KEY,
  projectId: process.env.DATAPULSE_PROJECT_ID,
  environment: 'production', // or 'staging'
  batchSize: 50,
  flushInterval: 10000 // ms
});

// Validate connection
await client.ping();
console.log('✓ Connected to DataPulse edge network');

3. Track Your First Event

Events are the core unit of data in DataPulse. Use the track() method to push structured events to your pipeline.

await client.track({
  event: 'user_signed_up',
  timestamp: new Date(),
  properties: {
    user_id: 'usr_8f3k29d',
    plan: 'enterprise',
    source: 'organic_search',
    metadata: { region: 'us-east-1', device: 'mobile' }
  }
});

console.log('✓ Event queued for ingestion');

4. Event Payload Structure

All events must conform to the following base structure. Optional fields enable advanced segmentation and attribution modeling.

Next Steps

Now that your pipeline is live, explore the Data Pipeline Architecture guide to understand ingestion flows, or head to the REST API Reference to programmatically manage datasets.