26 October 2025, UNWE
BDA1.1 Vs
Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets known as big data, in order to extract valuable insights.
Big data analytics allows you to detect trends, patterns, and correlations in large amounts of raw data to help analysts make data-driven decisions.
This process allows organizations to leverage the exponentially growing data generated from a variety of sources, including Internet of Things (IoT) sensors, social media, financial transactions, and smart devices, to extract actionable insights through advanced analytical techniques. Big data analytics is important because it allows organizations to use big data in a variety of formats from different sources to identify opportunities and risks, thereby helping organizations move quickly and improve their results.
Big data analytics cannot be limited to one tool or technology. Instead, several types of tools work together to help collect, process, clean, and analyze big data, and when combined with parallel processing, provide computing power for efficient processing and analysis.
• Hadoop
• NoSQL databases
• MapReduce
• YARN
Main parts of the course:
1. Definition and Characteristics of Big Data
2. Essence of Big Data Analytics
3. Data Analytics Methods and Approaches in Big Data Analytics
4. Big Data Analytics Tools and Technologies
5. Advantages and Good Practices in Implementing Big Data Analytics
6. Big Data Challenges and Development Trends
7. Technology Application – Hadoop, NoSQL databases, MapReduce
