Hadoop vs Spark: What to Choose to Process Big Data

With the impressive amount of information generated by modern society, big data becomes not just an investigation field, but a powerful force that changes business practices and marketing strategies. If you are interested in big data analysis, you have probably heard about Spark and Hadoop, two well-known distributed systems for data management and processing. At first glance, they might seem to work quite similar, but in fact, each of these frameworks has its own peculiarities and cases of application. In fact, the biggest difference between Spark and Hadoop is that the former works in-memory while the latter writes files to Distributed File System. Hadoop has the impressive processing power and is essential for any project that works with massive datasets, while Spark is famous for its speed and is effective for real-time data analysis. Do you want to learn more about these frameworks? Are you curious when to use Spark vs Hadoop? In our article, we will compare these two popular software frameworks so you can decide which one suits your project the best.

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