Spark Project on Cloudera Hadoop(CDH) and GCP for Beginners
Learn to build robust data pipelines with Apache NiFi, Kafka, Spark, Cassandra, MongoDB, Hive & Zeppelin. Master data processing & analytics.
Course Content
Spark Project on Cloudera Hadoop(CDH) and GCP for Beginners is an all-levels Udemy course taught by PARI MARGU. It runs 10h 51m, is taught in English, and includes a certificate of completion. Learners rate it 3.8 out of 5 from 122 reviews, with 1,595 students enrolled. It is a paid course, last updated April 2021.
Retail businesses, both brick-and-mortar stores and eCommerce websites, generate massive amounts of real-time data. Extracting valuable insights from this data is crucial for informed business decision-making, ultimately driving sales growth and enhancing customer experience in the competitive retail market. To effectively manage this high-volume, real-time data stream, a robust and scalable data processing pipeline is essential.
This project focuses on building a comprehensive data processing pipeline leveraging a powerful combination of open-source technologies:
- Apache NiFi: For data ingestion, routing, and transformation.
- Apache Kafka: A distributed event streaming platform designed for high-throughput, low-latency data feeds. Kafka acts as a central nervous system for your data.
- Apache Spark: An open-source unified analytics engine for large-scale data processing. Utilizing Scala and PySpark, Spark enables powerful data transformations and analytics on a distributed cluster.
- Apache Cassandra: A highly scalable NoSQL database designed for handling massive amounts of data with high availability.
- MongoDB: Another popular NoSQL database offering flexible document storage.
- Apache Hive: A data warehouse system built on top of Hadoop, providing a SQL-like interface for querying and analyzing large datasets.
- Apache Zeppelin: An interactive notebook that allows for data exploration, analysis, and visualization.
The Spark project is deployed on a Cloudera Hadoop (CDH 6.3) cluster running on Google Cloud Platform (GCP), providing a robust and scalable infrastructure for big data analytics.
Key Technologies & Concepts:
- Big Data Processing: Handling and analyzing extremely large datasets.
- Real-time Data Analytics: Processing data as it arrives to generate immediate insights.
- Data Pipelines: Automated workflows for moving and transforming data.
- Scalable Storage: Solutions for storing vast amounts of data.
- Distributed Computing: Processing data across multiple computers.
- NoSQL Databases: Databases that offer flexible data models, contrasting with traditional relational databases. These are well-suited for handling the variety and volume of data generated in retail.
- Event Streaming: A paradigm for building real-time data pipelines and streaming applications.
Want AI in your product? Hire the team behind TutorialSearch.io
LLM features, RAG, agents and automation wired into a real product — built by the devs who shipped this site.