Spark Project on Cloudera Hadoop(CDH) and GCP for Beginners

In retail business, retail stores and eCommerce websites generates large amount of data in real-time.

There is always a need to process these data in real-time and generate insights which will be used by the business people and they make business decision to increase the sales in the retail market and provide better customer experience.

Since the data is huge and coming in real-time, we need to choose the right architecture with scalable storage and computation frameworks/technologies.

Hence we want to build the Data Processing Pipeline Using Apache NiFi, Apache Kafka, Apache Spark, Apache Cassandra, MongoDB, Apache Hive and Apache Zeppelin to generate insights out of this data.

The Spark Project is built using Apache Spark with Scala and PySpark on Cloudera Hadoop(CDH 6.3) Cluster which is on top of Google Cloud Platform(GCP).

Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance.

Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.

Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.

A NoSQL (originally referring to "non-SQL" or "non-relational") database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.