Real Time Spark Project for Beginners: Hadoop, Spark, Docker
What you’ll learn
- Complete Development of Real Time Streaming Data Pipeline using Hadoop and Spark Cluster on Docker
- Setting up Single Node Hadoop and Spark Cluster on Docker
- Features of Spark Structured Streaming using Spark with Scala
- Features of Spark Structured Streaming using Spark with Python(PySpark)
- How to use PostgreSQL with Spark Structured Streaming
- Basic understanding of Apache Kafka
- How to build Data Visualisation using Django Web Framework and Flexmonster
- Fundamentals of Docker and Containerization
In many data centers, different type of servers generate large amount of data(events, Event in this case is status of the server in the data center) in real-time.
There is always a need to process these data in real-time and generate insights which will be used by the server/data center monitoring people and they have to track these server’s status regularly and find the resolution in case of issues occurring, for better server stability.
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 Real Time Data Pipeline Using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django and Flexmonster on Docker to generate insights out of this data.
The Spark Project/Data Pipeline is built using Apache Spark with Scala and PySpark on Apache Hadoop Cluster which is on top of Docker.
Data Visualization is built using Django Web Framework and Flexmonster.