Real-Time Operational Analytics API Integration
Summary
Freelancer Client is hiring: Real-Time Operational Analytics API Integration.
Location: Remote
I need help wiring a third-party data & analytics API into my existing business web application so that I can monitor operational metrics live. The goal is true real-time insight—think sub-second event ingestion, processing, and an endpoint my front-end can poll or subscribe to without noticeable lag.
What you'll do:
• Implement basic alerting when latency exceeds agreed thresholds.
Skills: PHP, JavaScript, Python, Node.js, Software Development, OAuth, Data Analytics, Backend Development, API Integration, REST API
Budget: $750–$1500 USD
Source: Freelancer Client via Remote / Online. Apply on the source website.
Original
I need help wiring a third-party data & analytics API into my existing business web application so that I can monitor operational metrics live. The goal is true real-time insight—think sub-second event ingestion, processing, and an endpoint my front-end can poll or subscribe to without noticeable lag.
Here’s what I already have: a working web app with a clean REST layer and a database that captures orders, inventory movements, and support tickets. What I am missing is a robust analytics pipeline that can pick those events up the moment they happen, enrich them if necessary, and expose aggregated KPIs such as order-pick latency, stock-out frequency, and average ticket resolution time.
Your core tasks
• Select or confirm a suitable data & analytics API that supports streaming or webhooks for real-time feeds.
• Integrate it with my back-end (language-agnostic, but the current stack is mostly JavaScript/Node).
• Authenticate securely (API keys or OAuth, depending on the platform).
• Map our operational events to the provider’s data model, set up the live ingestion endpoint, and expose a concise JSON payload my front-end can read.
• Implement basic alerting when latency exceeds agreed thresholds.
• Provide a short README explaining configuration, environment variables, and how to trigger a test event end-to-end.
Acceptance criteria
1. Operational metrics reach the analytics API in under 5 seconds from event creation.
2. Endpoints return aggregated KPIs in JSON with <200 ms response time for typical loads.
3. Error handling and retries cover network interruptions gracefully.
4. Clean, commented code commits plus the README are delivered through Git.
If you have experience with tools like Kafka, WebSockets, or any popular real-time analytics service (Datadog, AWS Kinesis/CloudWatch, Google Analytics Real-Time, etc.) you’ll hit the ground running. Let me know which API you would choose and why, and we can move forward right away.
Location & Details
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