InnovateTheExisting / Real-Time-Streaming-Data-Pipeline-and-Dashboard

Building Real Time Data Pipeline using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django and Flexomonster on Docker to track status of Servers in the Data Center across the Globe.

Building Streaming Data Pipeline using Apache Hadoop, Apache Spark and Kafka on Docker

Apache Hadoop, Apache Spark, Apache ZooKeeper, Kafka, Scala, Python, PySpark PostgreSQL, Docker, Django, Flexmonster, Real Time Streaming, Data Pipeline, Dashboard

About The Project

Building Real Time Data Pipeline Using Apache Kafka, Apache Spark, Hadoop, PostgreSQL, Django and Flexmonster on Docker

Project Description

• 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.

Use Case Diagram

Proposed Pipeline Architecture

Built With

• Apache Hadoop
• Apache Spark
• Apache ZooKeeper
• Docker
• Kafka
• Django (IDE uSed VS Studio Code)
• Flexmonster
• Scala (IDE used Idea IntellliJ Professional Edition)
• Python (IDE used PyCharm Professional Edition)
• PySpark (IDE used PyCharm Professional Edition)

Environment Setup

(a) Docker Setup

Installed Docker Toolbox (https://github.com/docker/toolbox/releases) for Windows 10 Home Edition Operating System. Faced the following issue after installation :

To fix the issue, first enable "Virtualization" from BIOS if it is disabled. Strangley, the "hypervisor" needed to be manually turned "Off", if it is turned "Auto". Use the following link to fix the issue if facing the same problem (docker-archive/toolbox#745).

After successful installation:

(b) Create Single Node Kafka Cluster in Local Machine

Run the following script in the created Docker Terminal:

Steps	Type Commands in Docker Terminal
1. Create Docker Network	docker network create --subnet=172.20.0.0/16 datamakingnet # create custom network
2. Create ZooKeeper Container	docker pull zookeeper:3.4s
	docker run -d --hostname zookeepernode --net datamakingnet --ip 172.20.1.3 --name datamaking_zookeeper --publish 2181:2181 zookeeper:3.4
3. Create Kafka Container	docker pull ches/kafka
	docker run -d --hostname kafkanode --net datamakingnet --ip 172.20.1.4 --name datamaking_kafka --publish 9092:9092 --publish 7203:7203 --env KAFKA_ADVERTISED_HOST_NAME=192.168.99.100 --env ZOOKEEPER_IP=192.168.99.100 ches/kafka
	docker images
	docker ps
	docker ps -a

Screenshot:

(C) Create Single Node Apache Hadoop and Spark Cluster on Docker

Run the following script in the created Docker Terminal:

Steps	Type Commands in Docker Terminal
1. Create Single Node Hadoop Cluster	Unzip and extract datamaking_hadoop_spark_cluster_image.zip file
	cd datamaking_hadoop_spark_cluster_image
2. Create docker images for Apache Hadoop, Apache Spark, Apache Hive and PostgreSQL	./1_create_hadoop_spark_image.sh
3. Create docker containers for Apache Hadoop, Apache Spark, Apache Hive and PostgreSQL	./2_create_hadoop_spark_cluster.sh createa
Stop all Containers	docker stop $(docker ps -a -q)
	docker rm $(docker ps -a -q)
Remove all Images	docker images -a
	docker rmi $(docker images -a -q)

Development Setup

(a) Event Simuator Using Python

Run the python script "data_center_server_status_simulator.py". This script simulates live creation of server status information.

(b) Building Streaming Data Pipeline using Scala and Spark Structured Streaming (Scala Based)

Load "datamaking_streaming_data_pipeline (Scala)" as a new scala project in Idea IntelliJ and run it.

OR

(b) Building Streaming Data Pipeline using Scala and Spark Structured Streaming (PySpark Based)

Open "datamaking_streaming_data_pipeline (PySpark)". Run the following pyspark script "real_time_streaming_data_pipeline.py".

(c) Setting up PostgreSQL Database(Events Database)

Login to docker terminal and the PostgreSQL CLI will be used.

Steps
psql -U postgres
CREATE USER demouser WITH PASSWORD 'demouser';
ALTER USER demouser WITH SUPERUSER;
CREATE DATABASE event_message_db;
GRANT ALL PRIVILEGES ON DATABASE event_message_db TO demouser;
\c event_message_db;

(d) Building Dashboard using Django Web Framework and Flexmonster for Visualization

Load the folder "Server Status Monitoring" in VS Code IDE as a new project and run it.

Final Result

The dashboard updates live as the python simultor keeps feeding with new data. Some sample screenshots:

Contact

Pritom Das Radheshyam - Portfolio Website