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Maximizing your event-driven architecture investments: Unleashing the power of Apache Kafka with IBM Event Automation

IBM Journey to AI blog

At the forefront of this event-driven revolution is Apache Kafka, the widely recognized and dominant open-source technology for event streaming. It offers businesses the capability to capture and process real-time information from diverse sources, such as databases, software applications and cloud services.

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Real-Time Sentiment Analysis with Kafka and PySpark

Towards AI

Within this article, we will explore the significance of these pipelines and utilise robust tools such as Apache Kafka and Spark to manage vast streams of data efficiently. Apache Kafka Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications.

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Unveiling Developers’ Technologies and Tools Usage in Large and Small and Medium-sized Enterprises…

Mlearning.ai

The focus of this investigation revolves around understanding their industry distribution, age demographics, developer types, and their adoption of various programming languages, databases, platforms, web frameworks, miscellaneous technologies, technical tools, new collaboration tools, and AI-powered search tools. NET Framework (1.0–4.8)’

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Real-time artificial intelligence and event processing  

IBM Journey to AI blog

IBM Event Automation is a fully composable solution, built on open technologies, with capabilities for: Event streaming : Collect and distribute raw streams of real-time business events with enterprise-grade Apache Kafka. Event endpoint management : Describe and document events easily according to the Async API specification.

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22 Widely Used Data Science and Machine Learning Tools in 2020

Analytics Vidhya

Overview There are a plethora of data science tools out there – which one should you pick up? Here’s a list of over 20. The post 22 Widely Used Data Science and Machine Learning Tools in 2020 appeared first on Analytics Vidhya.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

Typical examples include: Airbyte Talend Apache Kafka Apache Beam Apache Nifi While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering. Talend Free to use.

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Big data engineering simplified: Exploring roles of distributed systems

Data Science Dojo

Its characteristics can be summarized as follows: Volume : Big Data involves datasets that are too large to be processed by traditional database management systems. databases), semi-structured data (e.g., These datasets can range from terabytes to petabytes and beyond. XML, JSON), and unstructured data (e.g., text, images, videos).

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