Distributed Systems

Learn about Apache Spark, a unified analytics engine for big data processing that provides high performance and scalability for a wide range of use cases.

Apache Kafka is a distributed streaming platform that is used to build real-time data pipelines and streaming applications. It is horizontally scalable, fault-tolerant, and extremely fast. In this post, we will discuss how to use Apache Kafka in real-world scenarios.

The BASE principle is a set of design principles that help developers create more scalable and available distributed systems. In this article, we will explore the concept of the BASE principle, its origins, and its applications in distributed systems.

The CAP theorem is a fundamental principle in distributed systems that states that it is impossible to simultaneously achieve consistency, availability, and partition tolerance in a distributed system. In this article, we will explore the concept of the CAP theorem, its implications for distributed systems, and how developers can design systems that balance consistency, availability, and partition tolerance.

In this article, we will discuss the differences between ULID, UUIDv4, and UUIDv7, three popular types of unique identifiers used in software development. We will compare their characteristics, use cases, and advantages to help you choose the right type of identifier for your application.

Ensuring data consistency in a distributed system is crucial for maintaining the integrity and reliability of the system. Learn about the challenges of data consistency, strategies for achieving consistency, and best practices for designing distributed systems that prioritize data integrity.

In the distributed relational database model, two common models are multi-primary (master-master) and primary-replica (master-slave) replication. Let’s explore how each works, what replication does not give you, and when to use which.