The volume of data generated today is becoming enormous and hard to handle. Around 463 zettabytes of data are generated daily from various sources, including sensors, social media interactions, transactions, etc. (Source: Statista). However, the true value of data lies in real-time analytics that can help businesses act faster and stay ahead of their competition.
Apache Kafka and Apache Spark are two powerful technologies serving as the backbone of modern real-time data science pipelines. Kafka is a distributed event-streaming platform that helps applications to publish, subscribe, and process millions of events per second. It can handle multiple producers and consumers, which makes it best suited for use cases like fraud detection, log aggregation, and seamless data integration.
On the other hand, Spark is an open-source distributed computing system best known for its speed and versatility. It offers in-memory processing and support for batch, streaming, and machine learning tasks. Spark is a great tool that helps organizations get insights faster.
Businesses can build a robust data pipeline by integrating Kafka and Spark to collect, process, and analyze data in real time. Furthermore, adding machine learning models can enhance predictive capabilities and make data-driven decisions smarter and faster.
Learn how to set up and use these powerful data science tools for your data science workflow in our comprehensive guide.
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