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Apache Kafka use cases: Driving innovation across diverse industries

IBM Journey to AI blog

Apache Kafka is an open-source , distributed streaming platform that allows developers to build real-time, event-driven applications. With Apache Kafka, developers can build applications that continuously use streaming data records and deliver real-time experiences to users. How does Apache Kafka work?

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Data Warehousing A data warehouse is a centralised repository that stores large volumes of structured and unstructured data from various sources. It enables reporting and Data Analysis and provides a historical data record that can be used for decision-making.

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What is Data Ingestion? Understanding the Basics

Pickl AI

Data Ingestion Tools To facilitate the process, various tools and technologies are available. These tools can automate data collection, transformation, and loading processes, making it easier for organisations to manage their data pipelines effectively. Apache Kafka An open-source platform designed for real-time data streaming.

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A Comprehensive Guide to the main components of Big Data

Pickl AI

Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient data analysis across clusters. Data Lakes allows for flexibility in handling different data types.

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Training Models on Streaming Data [Practical Guide]

The MLOps Blog

We will also get familiar with tools that can help record this data and further analyse it. In the later part of this article, we will discuss its importance and how we can use machine learning for streaming data analysis with the help of a hands-on example. What is streaming data?

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A Comprehensive Guide to the Main Components of Big Data

Pickl AI

Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient data analysis across clusters. Data Lakes allows for flexibility in handling different data types.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data. Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing.