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In today’s data-driven world, two names consistently stand out when talking about large-scale data processing: Apache Kafka and Apache Hadoop. Both are open-source projects from the Apache Software Foundation, and while they often appear in the same ecosystem, they serve fundamentally different purposes.
This article explains what each technology does, how they differ, and how they can work together to power modern data architectures.
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In Computer Science, the term "serverless" refers to a cloud computing execution model in which the cloud provider dynamically manages the allocation and provisioning of servers. Despite the name, servers still exist—but the key idea is that developers don’t have to manage them directly.
Let’s unpack this carefully and technically:
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When people talk about programming languages, they often mention functional, imperative, or object-oriented styles. These aren’t just buzzwords — they describe different ways of thinking about computation. Understanding these paradigms helps you see why languages look and behave the way they do, and how to choose the right tool for your next project.
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Excellent question — and one with a surprisingly deep intellectual history that stretches back nearly a century, crossing logic, mathematics, and computer science. Let’s go through it step-by-step, from its origins in mathematical logic, through its adoption in programming languages, and up to the modern concept of “lambda functions” across today’s languages.
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The CAP theorem (also known as Brewer's theorem) is a fundamental principle in distributed systems that describes the inherent trade-offs when designing databases or data stores that operate across multiple networked nodes. It was conjectured by computer scientist Eric Brewer in 2000 during a keynote at the PODC symposium, and formally proved in 2002 by Seth Gilbert and Nancy Lynch at MIT.
The theorem states:
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The Transmission Control Protocol (TCP) is a cornerstone of the Internet, ensuring reliable, ordered, and error-free data delivery between applications over IP networks. Operating at the transport layer (Layer 4 of the OSI model), TCP is used in applications like web browsing (HTTP/HTTPS), email (SMTP), and file transfers (FTP), where data integrity is critical. Unlike the lightweight, connectionless UDP, TCP establishes a virtual connection, guarantees delivery, and manages network congestion, trading some speed for robustness. In this post, we'll dive into TCP's features, mechanisms, and internal workings, including its sliding window protocol for managing data flow.
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Here is a comprehensive comparison of gRPC (from Google) against its major alternatives — covering what gRPC is, when to use it, how it stacks up versus others, and what trade-offs exist. If you like, I can also pull in benchmark numbers and ecosystem maturity per language.
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Below is a single, exhaustive, expert-level comparison of Protocol Buffers (Protobuf) versus all mainstream serialization and data-interchange approaches — including both textual and binary, schema-based and schema-less, auto-generated and manual ones.
