JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD MCA 2017 BIG DATA ANALYTICS Previous Question Paper (August - 2017 - R15)

Big data analytics is the process of examining large and varied data sets to uncover hidden patterns, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions. It involves applying advanced analytic techniques such as predictive modeling, statistical analysis, and machine learning on big data.

Conversely, not big data analytics refers to traditional data processing methods that rely on structured data stored in relational databases (RDBMS), which can be efficiently managed using standard SQL queries and traditional business intelligence tools. It does not involve the high-velocity, high-variety, or massive-volume data characteristics that require distributed computing frameworks like Hadoop or Spark.

Data visualization tools are software applications that translate complex raw data into graphical representations such as charts, graphs, maps, and dashboards. These tools enable users to identify patterns, outliers, and trends that are difficult to discern in text-based data.

Key examples include:

• Tableau: Known for its powerful interactive dashboards and ability to connect to various data sources.
• Power BI: A Microsoft tool that integrates seamlessly with Excel and provides robust business intelligence capabilities.
• D3.js: A JavaScript library for producing dynamic, interactive data visualizations in web browsers.