Real-time analytics in IoT deployments relies on stream processing technology. Stream processing allows IoT systems to process data as it arrives, enabling real-time analysis, monitoring, and decision-making. It is essential for handling high-velocity data from IoT devices effectively.

In its early stages, the Internet of Things (IoT) was primarily focused on industrial automation. It aimed to improve efficiency and monitoring in various industries by connecting machines and sensors to the internet, enabling remote monitoring and control.

The concept of IoT can be traced back to the 1970s, even though it became more popular and widely recognized in the 21st century. The first internet-connected device was a Coke machine at Carnegie Mellon University in the early 1980s, but the foundational ideas began earlier.

Azure IoT Hub is a service that facilitates bi-directional communication between IoT applications and the devices it manages. Azure Functions, Azure IoT Suite, and Azure Data Factory serve different purposes within the Azure ecosystem.

ARPANET, the precursor to the modern internet, can be considered a precursor to the Internet of Things (IoT). ARPANET was one of the first wide-area packet-switching networks and laid the foundation for the interconnected networks that led to the development of IoT.

The scope and objectives of an IoT project are defined in the planning phase. During this phase, project goals, deliverables, and overall strategy are determined.

Monitoring equipment health and predicting machine failures is a prime example of predictive analytics. Predictive analytics uses historical data and statistical algorithms to make predictions about future events, helping companies prevent costly machine failures.

One of the earliest implementations of IoT can be traced back to Carnegie Mellon University, where a "wireless sensor network" was developed. Wireless sensor networks are a crucial component of IoT, as they enable the collection of data from various sensors and devices. These networks are a fundamental building block of IoT technology.

The Dependency Injection pattern involves passing objects as dependencies to the objects that require them, while the Service Locator pattern involves a central registry that provides objects when requested. The Dependency Injection pattern provides objects to its client as they are needed, while the Service Locator pattern allows objects to request objects they need from a centralized registry.

The Façade pattern provides a simplified interface to a complex system, while the Mediator pattern provides a way for components of a system to communicate with each other in a loosely coupled manner. Façade is a one-way communication pattern, while Mediator is a two-way communication pattern.