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Table 2 Overview of Software handling Big Data

From: Detecting Denial of Service attacks using machine learning algorithms

Tools/Software Advantages Disadvantages
ORANGE • It's a terrific technique for projecting demand while knowing the patterns and trends of five years' worth of data
• Working further insights into it, and hypothesis models testing of data projected by orange
• It isn't very reliable when dealing with massive datasets. Orange may crash if you use datasets that operate well in Python
• As a result, it's appropriate for smaller projects, teaching reasons, or exploratory data analysis
RAPIDMINER • it is a robust data mining application that can handle everything from data mining through model deployment and model operations
• Its end-to-end data science platform includes all of the data preparation and machine learning tools
• The programme has a tendency to crash frequently; this is especially true with neural networks and other complex algorithms. Some versions have limitations
• Even the student edition has a 10,000-row output restriction, so if you're trying to analyse a 12,000-point data set, 2000 points will be excluded at random
KNIME • Access to all current and future advancements in data science, machine learning, and artificial intelligence
• Avoid the danger of price changes by locking your data science IP into a proprietary format. Make data science accessible to everyone, not just Windows users
• The number of rows is unlimited, but the number of columns shouldn't get much larger than ~ 10.000
APACHE SPARK • Analytics is advanced
• Dynamic in nature
• Multilingual
• Powerful
• Fewer Algorithms
• Small files issue
• Window criteria
• Doesn’t suit well for multi-user environment
HADOOP • Minimum network traffic
• High throughput
• High Speed
• Fault tolerance
• Problem with small files
• Vulnerable
•Security issues
• Supports only batch processing
TENSORFLOW • Good for data visualization
• Scalable
• Compatible
• Inconsistent
• Less Speed
• Dependency
• Frequent Updates