Building Big Data Pipelines with PySpark + MongoDB + Bokeh (Udemy.com)
Build intelligent data pipelines with big data processing and machine learning technologies
Created by: EBISYS R&D
Last updated February 2020
What you will learn
- PySpark Programming
- Data Analysis
- Python and Bokeh
- Data Transformation and Manipulation
- Data Visualization
- Big Data Machine Learning
- Geo Mapping
- Geospatial Machine Learning
- Creating Dashboards
How it compares
Against the other 150 rated Udemy courses in Hadoop on CourseDuck:
- Rating4.5 out of 5higher than 68% of them
- Reviews71#124 of 151 by number of reviews
- Length5.1 hoursshorter than 57% of them
- Last updatedFebruary 2020earlier than 72% of them
Course Description
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
Instructor Details
- 4.5 Rating
71 Reviews
EBISYS R&D
Big Data Engineering and Consulting, 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.
Currently 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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Reviews
By Evgenia Levin on 7/8/2023
I am disappointed because it's outdated in terms of the code, it takes too much time to adjust the code to the relevant syntax and it's not worth it. There are also very minimal explanations. I had to research every step myself to understand why it's done and then spend hours adjusting the code to run.
By Kaustubh Surendra Gode on 6/4/2023
Update the course with time , faced error in end lessons
By Marcos on 10/6/2022
El curso es correcto pero la asistencia del profesor es escasa. He tenido problemas para ejecutar los scripts de los apartados 22, 23 y 24.
By Harry Pruman on 3/26/2021
Really enjoy the straight forward style so we can actually code and get some repetitions utilizing functions rather than focusing on some of the underlying concepts which can be studied outside the course
By Rez on 9/23/2020
Covered materials are not sufficient to make learner comfortable with the spark and Apache eco. system. I didn't learn enough by following this course
By Aditya on 5/5/2020
some time we face errors which takes lot of time
By Jogendra Singh Khalsa on 7/13/2024
By Shuting Kang on 9/17/2022
By Rosa Sampaio Silva on 4/1/2020
Quality Score
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Overall Score : 90 / 100






