Building Big Data Pipelines with PySpark + MongoDB + Bokeh

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

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Last Updated: 2022-11-18 06:11:32 English Course Type: Self Learning

PySpark Programming
Data Analysis
Python and Bokeh
Data Transformation and Manipulation
Data Visualization
Big Data Machine Learning
Geo Mapping
Geospatial Machine Learning
Creating Dashboards

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

Basic Understanding of Python
Little or no understanding of GIS
Basic understanding of Programming concepts
Basic understanding of Data
Basic understanding of what Machine Learning is

Python Developers at any level
Developers at any level
Machine Learning engineers at any level
Data Scientists at any level
The curious mind
GIS Developers at any level

Section 1 : Introduction 1 Lectures 00:09:30
Lecture 1 :
Introduction Preview
Section 2 : Setup and Installations 7 Lectures 00:36:51
Lecture 1 :
Python Installation
Lecture 2 :
Installing Third Party Libraries
Lecture 3 :
Installing Apache Spark
Lecture 4 :
Installing Java (Optional)
Lecture 5 :
Testing Apache Spark Installation
Lecture 6 :
Installing MongoDB
Lecture 7 :
Installing NoSQL Booster for MongoDB
Section 3 : Data Processing with PySpark and MongoDB 4 Lectures 00:33:15
Lecture 1 :
Integrating PySpark with Jupyter Notebook
Lecture 2 :
Data Extraction
Lecture 3 :
Data Transformation
Lecture 4 :
Loading Data into MongoDB
Section 4 : Machine Learning with PySpark and MongoDB 3 Lectures 00:20:05
Lecture 1 :
Data Pre-processing
Lecture 2 :
Building the Predictive Model
Lecture 3 :
Creating the Prediction Dataset
Section 5 : Data Visualization 5 Lectures 00:18:15
Lecture 1 :
Loading the Data Sources from MongoDB
Lecture 2 :
Creating a Map Plot
Lecture 3 :
Creating a Bar Chart
Lecture 4 :
Creating a Magnitude Plot
Lecture 5 :
Creating a Grid Plot
Section 6 : Building the Data Pipeline Scripts 4 Lectures 00:05:05
Lecture 1 :
Installing Visual Studio Code
Lecture 2 :
Creating the PySpark ETL Script
Lecture 3 :
Creating the Machine Learning Script
Lecture 4 :
Creating the Dashboard Server
Section 7 : Source Code and Notebook 1 Lectures
Lecture 1 :
Source Code and Notebook

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