1. Designing Event-Driven Systems

For as long as we’ve been talking about services, we’ve been talking about data. In fact, before we even had the word microservices in our lexicon, back when it was just good old-fashioned service-oriented architecture, we were talking about data: how to access it, where it lives, who “owns” it. Data is all-important—vital for the continued success of our business—but has also been seen as a massive constraint in how we design and evolve our systems.

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2. Making sense of stream processing
The idea of structuring data as a stream of events is nothing new, and it is used in many fields. Even though the underlying principles are often similar, the terminology is frequently inconsistent across different fields, which can be quite confusing. Although the jargon can be intimidating when you first encounter it, don’t let that put you off; many of the ideas are quite simple when you get down to the core.
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3. Apache Kafka for Dummies

Modern data requirements are real time. This is the case with processing online transactions or social media feeds, capturing Internet of Things (IoT) data, or generating “just-in-time” or instant analytics on any type of database transaction. Rapid, incremental, and high-volume changes in data require ultra-fast replication or processing. Traditional batch process database transactions, often slowing or pausing produced replication on source databases. Batch processes struggle to support real-time and continuous changes in databases.

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4. Confluent Definitive Guide

It’s an exciting time for Apache Kafka. Kafka is being used by tens of thousands of organizations, including over a third of the Fortune 500 companies. It’s among the fastest growing open source projects and has spawned an immense ecosystem around it. It’s at the heart of a movement towards managing and processing streams of data.

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5. I Heart Logs ebook

Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention.

Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models.

Go ahead and take the plunge with logs: you’re going to love them.

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6. Five steps to Event Streaming
Plenty of businesses make an initial toe-dip into event streaming to solve for a particular task that requires moving large amounts of data around. This sort of bottom-up, or developer- led, entree is a typical start to the adoption journey of event streaming. But if the platform never reaches the sight line of company leadership, it may be blocked from reaching its full potential—and the organization may not truly capitalize on data, or event streaming, to transform.

There’s profound strategic potential in an event streaming platform for enterprise businesses of many kinds. The types of business challenges’ event streaming is capable of addressing include driving better customer experience, reducing costs, mitigating risk, and providing a single source of truth across the business. It can be a game changer.

Here are the five crucial steps to turning event streaming into a business driver and digital transformer.

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10 Principles for Streaming Services
As streaming data becomes an increasingly significant factor for modern, digital-age businesses, organizations need flexible tools for managing data streams efficiently and in real-time. Microservices architectures enable businesses to evolve their systems away from the slow and unresponsive shared-state architectures of the past. Businesses can deploy a microservice- based environment either with event-based or request-response approaches, or a hybrid of the two. The trend in business today is towards hybrid or predominantly event-driven architectures, in which the services themselves raise events.
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