Welcome to the Building Big Data Pipelines with SparkR & Tableau & MongoDB course. In this course we will be creating a big data analytics solution using big data technologies for R.

In our use case we will be working with raw earthquake data and we will be applying big data processing techniques to extract transform and load the data into usable datasets. Once we have processed and cleaned the data, we will use it as a data source for building predictive analytics and visualizations.

Tableau Desktop is a powerful data visualization tool, used for big data analysis and visualization. It allows for data blending, real-time analysis and collaboration of data. No programming is needed for Tableau Desktop, which makes it a very easy and powerful tool to create dashboards apps and reports.

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, dplyr) but on large datasets. SparkR also supports distributed machine learning using MLlib.

MongoDB is a document-oriented NoSQL database, used for high volume data storage. It stores data in JSON like format called documents, and does not use row/column tables. The document model maps to the objects in your application code, making the data easy to work with.

• You will learn how to create big data processing pipelines using R and MongoDB

• You will learn machine learning with geospatial data using the SparkR and the MLlib library

• You will learn data analysis using SparkR, R and Tableau

• You will learn how to manipulate, clean and transform data using Spark dataframes

• You will learn how to create Geo Maps in Tableau Desktop

• You will also learn how to create dashboards in Tableau Desktop