Building Big Data Pipelines with PySpark + MongoDB + Bokeh
What you'll 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
Requirements
- 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
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
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
Instructor
- 4.3 Instructor Rating
- 351 Reviews
- 8,180 Students
- 16 Courses
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.