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How to Use Kafka for Event Streaming in a Microservices Architecture?

Workfall

It means that there is a high risk of data loss but Apache Kafka solves this because it is distributed and can easily scale horizontally and other servers can take over the workload seamlessly. We want to always have unified data which is broadcasted to all the client applications simultaneously without any latency. Let’s get started!

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Data Mesh Architecture: Revolutionizing Event Streaming with Striim

Striim

Data Mesh is revolutionizing event streaming architecture by enabling organizations to quickly and easily integrate real-time data, streaming analytics, and more. In this article, we will explore the advantages and limitations of data mesh, while also providing best practices for building and optimizing a data mesh with Striim.

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On-Premise vs Cloud: Where Does the Future of Data Storage Lie?

Monte Carlo

This often occurs when data teams don’t have the right cost monitors in place, and there is a particularly aggressive outlier event (perhaps bad code or exploding JOINs). More common however, is that cost or performance optimization becomes the wild west until a particularly eye-watering bill hits the CFO’s desk.

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A Dive into Apache Flume: Installation, Setup, and Configuration

Analytics Vidhya

Introduction Apache Flume is a tool/service/data ingestion mechanism for gathering, aggregating, and delivering huge amounts of streaming data from diverse sources, such as log files, events, and so on, to centralized data storage. Flume is a tool that is very dependable, distributed, and customizable.

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We’ll See You at the Gartner Data and Analytics Summit

Cloudera

We’re at a crucial point in time where trusted data is fundamental for driving new AI use cases, enabling real-time operations, and allowing enterprises to easily scale. With that, let’s take a closer look at what the Cloudera team will be doing over the course of the event.

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Big Data Technologies that Everyone Should Know in 2024

Knowledge Hut

Each of these technologies has its own strengths and weaknesses, but all of them can be used to gain insights from large data sets. As organizations continue to generate more and more data, big data technologies will become increasingly essential. Let's explore the technologies available for big data.

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How to Design a Modern, Robust Data Ingestion Architecture

Monte Carlo

In batch processing, this occurs at scheduled intervals, whereas real-time processing involves continuous loading, maintaining up-to-date data availability. Data Validation : Perform quality checks to ensure the data meets quality and accuracy standards, guaranteeing its reliability for subsequent analysis.