Streams

Reactive Programming for Responsive Apps Reactive programming helps apps respond to user actions and data changes without blocking the UI. It uses streams of events and data that flow through the app. When something changes, parts of the app react automatically. The core idea is to treat events as data. An input, a network reply, or a timer can be a stream. Observers listen, and operators transform, combine, or filter these streams. This approach makes the UI stay responsive even during busy work. ...

Big Data Foundations: Storage, Processing, and Analytics Big data projects rest on three foundations: storage, processing, and analytics. Each part answers a simple question. Where is the data kept? How is it transformed? What can we learn from it? Together they form a practical path from raw logs to useful insights. Storage basics Data first needs a safe, scalable home. Many teams use object storage in the cloud or on premises, often called a data lake. Key ideas include: ...

From Requirements to Code A Reactive Development Approach Developing software from a long list of requirements can feel like stitching clues. A reactive development approach shifts focus from screens to flows. It helps teams turn ideas into responsive code by modeling data and events as streams. The result is a product that adapts as things change. Starting with clear goals Start by defining what success looks like in real terms. For example, “the dashboard updates within 200 ms” or “new data appears within two seconds.” Keep requirements small, testable, and tied to user outcomes. Document acceptance criteria, not just features. Align the team around a simple story that ends with a visible, verifiable result. ...

Reactive Programming for Responsive Systems Modern apps face growing demand for speed and reliability. Reactive programming helps by treating data as a continuous flow and reacting to events as they happen. This mindset keeps systems responsive, even under bursty traffic, and makes maintenance easier over time. What makes a system responsive Latency matters. Users notice delays in interfaces, APIs, and background tasks. A responsive system absorbs bursts, keeps interactions smooth, and recovers quickly from failures. By separating producers from consumers and avoiding blocking work, you gain elasticity and easier testing. ...

Real-Time Analytics: Streaming Data at Scale Real-time analytics means turning events into insights as they arrive. Streaming data comes from logs, sensors, apps, and transactions. When processed with low latency, teams can detect issues, guide decisions, and trigger timely actions. At scale, the goals include steady throughput, predictable response times, and resilient operation. This requires well designed pipelines, careful partitioning, and reliable state management. A typical pipeline starts with data sources feeding a streaming platform. The processor keeps state, applies calculations, and emits results to storage, dashboards, or alerts. The key is to balance speed with correctness, especially when events arrive out of order or late. Good design also helps you handle bursts and keep the system responsive. ...