Building Big Data Pipelines with PySpark + MongoDB + Bokeh
Build intelligent data pipelines with big data processing and machine learning technologies
Updated on Nov, 2024
Language - English
Development ,Data Science,Big Data
Lectures -25
Resources -1
Duration -5 hours
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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 in 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 data processing using PySpark, Predictive modeling 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 data frames.
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.
Who this course is for?
- 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.
Goals
PySpark Programming.
Data Analysis.
Python and Bokeh.
Data Transformation and Manipulation.
Data Visualization.
Big Data Machine Learning.
Geo Mapping.
Geospatial Machine Learning.
Creating Dashboards.
Prerequisites
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.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction
09:30
09:30
Setup and Installations
6 Lectures
Data Processing with PySpark and MongoDB
4 Lectures
Machine Learning with PySpark and MLlib
3 Lectures
Data Visualization
6 Lectures
Creating the Data Pipeline Scripts
4 Lectures
Source Code and Notebook
1 Lectures
Instructor Details
Edwin Bomela
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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