Scala and Spark for Big Data Analytics

Inside the data analytics process

Data analytics applications involve more than just analyzing data. Before any analytics can be planned, there is also a need to invest time and effort in collecting, integrating, and preparing data, checking the quality of the data and then developing, testing, and revising analytical methodologies. Once data is deemed ready, data analysts and scientists can explore and analyze the data using statistical methods such as SAS or machine learning models using Spark ML. The data itself is prepared by data engineering teams and the data quality team checks the data collected. Data governance becomes a factor too to ensure the proper collection and protection of the data. Another not commonly known role is that ...