Distributed-Systems

Edge Computing: Processing at the Edge Edge computing moves data work closer to where it happens. Instead of sending every signal to a distant data center, devices learn to process data locally and act on it. This reduces latency, saves bandwidth, and can help when networks are slow or offline. In practice, an edge setup combines hardware at the edge with software that runs analytics, filters data, and triggers actions. Sensors generate data, a local gateway or computer processes it, and only useful results travel onward to the cloud. ...

Edge Computing: Processing at the Edge Edge computing is the practice of moving compute and data processing closer to devices and sensors. Instead of sending every bit to a central cloud, you run software on devices, gateways, or nearby servers. This reduces round trips, speeds up decisions, and helps work offline when the network is slow or intermittent. What is edge computing? Edge computing places processing near the source. Small devices, gateways, or micro data centers handle data before it travels far. This shortens response times and lowers bandwidth use. ...

Microservices Architecture and System Design Microservices turn a large software system into a set of small, independent services. Each service owns its own data and runs in its own process, which helps teams deploy updates faster and scale parts of the system as needed. But with more boundaries come more complexity: network calls, data consistency, and operational overhead. Key design principles help keep the architecture sane: Clear service boundaries aligned to business capabilities Autonomous deployment and small, reversible changes API-first contracts and stable versions Decentralized data ownership per service Resilience patterns: retries, timeouts, circuit breakers Observability: logs, metrics, distributed tracing Security by design: authentication, authorization, encryption Decomposition patterns guide how you split the system: ...

Databases Demystified: From SQL to NoSQL Databases are the backbone of most applications. They store data, keep it safe, and let apps fetch it quickly. Today, two major families dominate the scene: SQL databases, built around tables and a fixed schema, and NoSQL databases, which come in several types and favor flexibility. Both have strengths, and the right choice depends on how you plan to use data. Relational databases organize data in tables with columns and rows. The schema describes what data looks like, and relationships connect tables with keys. This structure makes it easy to enforce rules and to run reliable transactions. When you need consistency and strong data integrity, SQL shines. Typical queries look like: SELECT name FROM users WHERE id = 1; These systems generally use ACID guarantees to keep data safe during updates. ...

Relational vs NewSQL Databases Relational databases organize data in tables with rows and columns. They use SQL for queries and enforce ACID properties to guarantee correctness even under heavy load. They are proven, with wide tooling, and fit transactional apps, reporting, and dashboards. The model is familiar, and the community support is strong. Most teams start here because the guarantees are clear and the data model stays stable. Relational databases today For many businesses, relational DBs remain enough. They scale vertically well and offer powerful joins, aggregates, and constraints. The downside is that scaling out across many machines can be complex and costly. ...

Database Design Patterns for Scale Scale is not just hardware. It starts with data, access patterns, and how you recover from failures. When traffic grows, small mistakes in design become costly. The right database patterns help you meet performance goals while keeping data safe and consistent. This guide shares practical patterns you can apply to many apps, from microservices to large platforms. Data modeling matters more at scale. Normalization helps keep data clean, but very large systems often benefit from denormalization and read models. In practice, keep the source of truth in a durable store and create fast, read-optimized copies for queries and dashboards. For example, store order totals in a dedicated read model so checkout does not join many tables. ...

Big Data Essentials: Storage, Processing, and Insight Big data projects help teams turn large, diverse data into useful insights. The goal is to keep data reliable, accessible, and timely. This guide covers three essentials: storage, processing, and insight, with practical ideas you can apply today. Storage decisions shape cost, speed, and governance. A modern approach often uses a data lake built on object storage (Amazon S3, Azure Blob, Google Cloud Storage). This setup handles raw data in its native form and scales cheaply. For fast analytics, a data warehouse or lakehouse can host curated tables with schemas and indexes. The key is to separate raw data from processed data, so you can reprocess later without wasting time. Plan for metadata, lineage, and access controls to keep data discoverable and secure. ...

Edge Computing: Processing Data at the Data’s Edge Edge computing moves processing closer to where data is created. Instead of sending every sensor reading to a distant cloud, you run analytics on nearby devices, gateways, or local servers. This reduces latency, cuts bandwidth use, and can improve privacy when sensitive data stays local. How it works Edge setups connect sensors to a small computer at the edge. This device runs software that collects data, runs quick analyses, and makes decisions. If needed, only useful results or anonymized summaries travel onward to the cloud for long-term storage or wider insights. Common components are sensors, an edge gateway, an edge server, and a cloud link. ...

Database Scaling: Sharding and Replication Scaling a database means handling more users, more data, and faster queries without slowing down the service. Two common methods help achieve this: sharding and replication. They answer different questions—how data is stored and how it is served. Sharding splits the data across multiple machines. Each shard holds a subset of the data, so writes and reads can run in parallel. Common strategies are hash-based sharding, where a key like user_id determines the shard, and range-based sharding, where data is placed by a value interval. Pros: higher write throughput and easier capacity growth. Cons: cross-shard queries become harder, and rebalancing requires care. A practical tip is to choose a shard key that distributes evenly and to plan automatic splitting when a shard grows. ...

APIs and Middleware: Connecting Distributed Systems APIs and middleware work together to connect services spread across teams, clouds, and data centers. When done well, they let each part of a system talk without knowing the whole picture. This makes updates safer and deployments faster. The goal is clear contracts, reliable delivery, and good visibility. APIs are the surface you expose to other teams and services. They define what you can do, not how you do it. Middleware sits between apps, handling concerns like routing, transformation, and security. Together they reduce coupling and share responsibilities, so teams can evolve their parts independently. ...