Building Big Data Pipelines with SparkR & PowerBI & MongoDB

About Building Big Data Pipelines with SparkR & PowerBI & MongoDB

Welcome to the

Building Big Data Pipelines with SparkR & PowerBI & 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.

Power BI Desktop

is a powerful data visualization tool that lets you build advanced queries, models and reports. With Power BI Desktop, you can connect to multiple data sources and combine them into a data model. This data model lets you build visuals, and dashboards that you can share as reports with other people in your organization.

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 PowerBI

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

You will learn how to create Geo Maps in PowerBI Desktop

You will also learn how to create dashboards in PowerBI Desktop