Big Data and Data Analytics are related but distinct topics in the field of data science.

Big Data refers to the large and complex data sets that are generated by organizations and individuals today. It includes structured, semi-structured and unstructured data. The volume, variety, and velocity of big data make it difficult to process using traditional data processing tools.

Data Analytics, on the other hand, is the process of examining, cleaning, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. It includes a wide range of techniques and technologies such as statistical analysis, data visualization, machine learning, and predictive modeling.

Big Data Analytics is the application of data analytics techniques to big data to extract insights and knowledge from it. It helps organizations to identify patterns, trends, and insights that can support strategic decision-making and business operations.

Some popular technologies and tools used in big data and data analytics include Hadoop, Spark, NoSQL databases, and data visualization tools such as Tableau and Power BI.

Big Data and Data Analytics are becoming increasingly important in many industries, including finance, healthcare, retail, and telecommunications, among others. As data is becoming the new oil and a strategic asset for companies, having the ability to process and analyze big data is becoming a requirement for many organizations.