Building Big Data Pipelines with PySpark + MongoDB + Bokeh

Building Big Data Pipelines with PySpark + MongoDB + Bokeh

Building Big Data Pipelines with PySpark + MongoDB + Bokeh

Regular price Rs. 1,250.00 Sale price Rs. 599.00 Save 52%
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5 hours on-demand video
1 article
Full lifetime access
Access on mobile and Laptop
● Edwin Bomela

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


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

● 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

1. Introduction
2. Setup and Installations
3. Data Processing with PySpark and MongoDB
4. Machine Learning with PySpark and MLlib
5. Data Visualization
6. Creating the Data Pipeline Scripts
7. Source Code and Notebook

Edwin Bomela is a Big Data Engineer and Consultant, involved in multiple projects ranging from Business Intelligence, Software Engineering, IoT and Big data analytics. Expertise are in building data processing pipelines in the Hadoop and Cloud ecosystems and software development. He is currently a consulting at one of the top business intelligence consultancies helping clients build data warehouses, data lakes, cloud data processing pipelines and machine learning pipelines. The technologies he uses to accomplish client requirements range from Hadoop, Amazon S3, Python, Django, Apache Spark, MSBI, Microsoft Azure, SQL Server Data Tools, Talend and Elastic MapReduce.

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