Each project comes with 2-5 hours of micro-videos explaining the solution.
Code & Dataset
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
Understanding the roadmap of the project
Downloading and Installing the Yelp Datset
Understanding Elastic Search, downloading and Installing elastic search for analytics
Installing Kibana for Visualization of data using Elastic Search
Ingesting data from a relational database using Sqoop
Understanding Postman as a complete API for big data
Use of Spark and Elastic Search in Stack
Ingesting data from the relational database directly into Spark
Integrating of JDBC with Spark for connecting and executing the query with database
Exploring the dataset using HUE
How to load a Parquet file
Processing relational data in Spark
How to Map data
Creating UDFs by using the datasets
Understanding different data types supported by Elastic Search and working with them
Ingesting processed data into Elasticsearch
Visualizing user signup trend by creating histograms in Kibana
Loading and Denormalizing business table data
Most businesses seek to get reviews on their goods and services one way or another. It is a most basic way for the business to improve their efficiency and subsequently their bottom-line. Get the review is not only the issue, ability to extract and visualize analytics from review data is critical to business success.
In Apache Spark Project, we will use the yelp review dataset to analyze businesses and reviews over a period of time. Perhaps we will spot potential gaps in service delivery or see how business thrive in different scenarios.
Beyond processing this data, we will ingest the final output of our data processing in Elasticsearch and use the visualization tool in the ELK stack to visualize various kinds of ad-hoc reports from the data.