Building Big Data Pipelines with PySpark + MongoDB + Bokeh

Build intelligent data pipelines with big data processing and machine learning technologies

Ratings: 3.91 / 5.00

Welcome to the Building Big Data Pipelines with PySpark & MongoDB & Bokeh course. In

this course we will be building an intelligent data pipeline using big data technologies like

Apache Spark and MongoDB.

We will be building an ETLP pipeline, ETLP stands for Extract Transform Load and Predict.

These are the different stages of the data pipeline that our data has to go through in order for it

to become useful at the end. Once the data has gone through this pipeline we will be able to

use it for building reports and dashboards for data analysis.

The data pipeline that we will build will comprise of data processing using PySpark, Predictive

modelling using Spark’s MLlib machine learning library, and data analysis using MongoDB and

Bokeh.

You will learn how to create data processing pipelines using PySpark
You will learn machine learning with geospatial data using the Spark MLlib library
You will learn data analysis using PySpark, MongoDB and Bokeh, inside of jupyter notebook
You will learn how to manipulate, clean and transform data using PySpark dataframes
You will learn basic Geo mapping
You will learn how to create dashboards
You will also learn how to create a lightweight server to serve Bokeh dashboards