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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.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
In this spark project, we will measure by how much NFP has triggered moves in past markets.
In this hive project, you will design a data warehouse for e-commerce environments.