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https://github.com/aymane-maghouti/big-data-project
This project aims to predict smartphone prices using a combination of batch and stream processing techniques in a Big Data environment. The architecture follows the Lambda Architecture pattern, providing both real-time and batch processing capabilities to users.
https://github.com/aymane-maghouti/big-data-project
apache-airflow apache-kafka apache-spark batch-processing big-data-projects hbase hdfs ingestion java lambda-architecture machine-learning postgresql-database powerbi pyspark python spring-boot streaming
Last synced: 7 days ago
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This project aims to predict smartphone prices using a combination of batch and stream processing techniques in a Big Data environment. The architecture follows the Lambda Architecture pattern, providing both real-time and batch processing capabilities to users.
- Host: GitHub
- URL: https://github.com/aymane-maghouti/big-data-project
- Owner: aymane-maghouti
- Created: 2024-04-02T02:33:48.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-04-15T09:08:08.000Z (10 months ago)
- Last Synced: 2025-02-02T01:31:47.798Z (19 days ago)
- Topics: apache-airflow, apache-kafka, apache-spark, batch-processing, big-data-projects, hbase, hdfs, ingestion, java, lambda-architecture, machine-learning, postgresql-database, powerbi, pyspark, python, spring-boot, streaming
- Language: Python
- Homepage: https://youtu.be/iClZyC_TZyA
- Size: 960 KB
- Stars: 10
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Smartphone Price Prediction in Big Data Environment
## Table of Contents
1. [Project Overview](#1-project-overview)
2. [Technologies Used](#2-technologies-used)
3. [Architecture](#3-architecture)
4. [Repository Structure](#4-repository-structure)
5. [Software Requirements for Running the Project](#5-software-requirements-for-running-the-project)
6. [How to Run](#6-how-to-run)
7. [Dashboards](#7-dashboards)
8. [Acknowledgments](#8-acknowledgments)
9. [Conclusion](#9-conclusion)
10. [Contacts](#10-contacts)
## 1. Project Overview
This project aims to predict smartphone prices using a combination of batch and stream processing techniques in a Big Data environment. The architecture follows the Lambda Architecture pattern, providing both real-time and batch processing capabilities to users.
## 2. Technologies Used
* **Ingestion Layer:** Apache Kafka (message broker)
* **Stream Layer:** XGBoost (machine learning model), Apache HBase (real-time View)
* **Batch Layer:** Apache Spark (data processing framework), Apache Airflow (workflow orchestration), PostgreSQL (data warehouse (Batch View))
* **Visualization:** Spring Boot (web application framework), Power BI (interactive dashboards)
## 3. Architecture
- Here is the architecture :
The project architecture consists of five main layers: the ingestion layer, the batch layer, the stream layer, the serving layer and the visualization layer.
### Ingestion Layer
- **Apache Kafka**: Utilized for real-time data ingestion from an API providing smartphone data.
- **Consumer**: Collects data from the API and feeds it into the stream and batch layer.
### Stream Layer
- **Producer**: A machine learning model developed using XGBoost to estimate smartphone prices. This model runs in real-time and stores predictions in a realtime view. (details about the model here )
### Batch Layer
- **HDFS**: Data from the API is stored in HDFS as part of the data lake solution.
- **PySpark**: Performs data transformation on stored data using PySpark.
- **Apache Airflow**: Orchestrates the batch processing workflow.
### Serving Layer
- **Realtime View**: Implemented using HBase to provide real-time access to predicted smartphone prices.
- **Batch View**: Transformed data is stored in PostgreSQL, as the data warehouse solution.
### Visualization Layer
- **Spring Boot Web Application**: Provides a user interface to view real-time smartphone prices.
- **Power BI Dashboard**: Provides batch users with a visualization of processed data.
## 4. Repository Structure
The repository is organized as follows:
```batch
Big-Data-Project:.
| README.md
|
+---images
| architecture.png
| dashboard_phone.png
| run_web_app.png
| spring_boot_web_app.png
|
\---Main
| commands.sh
| Dashboard.pbix
|
+---.idea
| workspace.xml
|
+---Lambda
| | docker-compose.yaml
| | producer.py
| | transform.py
| |
| +---.idea
| | | .gitignore
| | | .name
| | | misc.xml
| | | modules.xml
| | | price prediction (big data envirnment).iml
| | | vcs.xml
| | | workspace.xml
| | |
| | \---inspectionProfiles
| | profiles_settings.xml
| |
| +---Batch_layer
| | | batch_layer.py
| | | batch_pipeline.py
| | | HDFS_consumer.py
| | | put_data_hdfs.py
| | | save_data_postgresql.py
| | | spark_tranformation.py
| | | __init__.py
| | |
| | +---dags
| | | syc_with_Airflow.py
| | | __init__.py
| | |
| | \---__pycache__
| | batch_layer.cpython-310.pyc
| | HDFS_consumer.cpython-310.pyc
| | put_data_hdfs.cpython-310.pyc
| | save_data_postgresql.cpython-310.pyc
| | spark_tranformation.cpython-310.pyc
| | __init__.cpython-310.pyc
| |
| +---ML_operations
| | | xgb_model.pkl
| | |
| | \---__pycache__
| +---real_time_web_app(Flask)
| | | app.py
| | | get_Data_from_hbase.py
| | |
| | +---static
| | | +---css
| | | | style.css
| | | |
| | | \---js
| | | script.js
| | |
| | +---templates
| | | index.html
| | |
| | \---__pycache__
| | get_Data_from_hbase.cpython-310.pyc
| |
| +---Stream_data
| | | stream_data.csv
| | | stream_data.py
| | |
| | \---__pycache__
| +---Stream_layer
| | insert_data_hbase.py
| | ML_consumer.py
| | stream_pipeline.py
| | __init__.py
| |
| \---__pycache__
| producer.cpython-310.pyc
| transform.cpython-310.pyc
|
\---real_time_app(Spring boot)
| .classpath
| .gitignore
| .project
| HELP.md
| mvnw
| mvnw.cmd
| pom.xml
|
+---.mvn
| \---wrapper
| maven-wrapper.jar
| maven-wrapper.properties
|
+---.settings
| org.eclipse.core.resources.prefs
| org.eclipse.jdt.core.prefs
| org.eclipse.m2e.core.prefs
|
+---src
| +---main
| | +---java
| | | \---com
| | | \---example
| | | \---demo
| | | | RealTimeAppApplication.java
| | | |
| | | +---controller
| | | | IndexController.java
| | | |
| | | \---service
| | | HbaseService.java
| | |
| | \---resources
| | | application.properties
| | |
| | +---static
| | | +---css
| | | | style.css
| | | |
| | | \---js
| | | script.js
| | |
| | \---templates
| | index.html
| |
| \---test
| \---java
| \---com
| \---example
| \---demo
| RealTimeAppApplicationTests.java
|
\---target
+---classes
| | application.properties
| |
| +---com
| | \---example
| | \---demo
| | | RealTimeAppApplication.class
| | |
| | +---controller
| | | IndexController.class
| | |
| | \---service
| | HbaseService.class
| |
| +---META-INF
| | | MANIFEST.MF
| | |
| | \---maven
| | \---com.example
| | \---real_time_app
| | pom.properties
| | pom.xml
| |
| +---static
| | +---css
| | | style.css
| | |
| | \---js
| | script.js
| |
| \---templates
| index.html
|
\---test-classes
\---com
\---example
\---demo
RealTimeAppApplicationTests.class
```
## 5. Software Requirements for Running the Project
This project requires the following software to be installed and configured on your system:
**Big Data Stack:**
* **Apache Kafka (version 2.6.0)**
* **Apache HBase (version 1.2.6)**
* **Apache Hadoop (version 2.7.0)**
* **Apache Spark (version 3.3.4)**
* **PostgreSQL database**
**Programming Languages and Frameworks:**
* **Python (version 3.10.x or later)**
* **Java 17 (or compatible version)**
* **Spring Boot**
**Machine Learning Library:**
* **XGBoost**
**Additional Tools:**
* **Apache Airflow**
* **Power BI Desktop**
By installing and configuring these tools, you will have the necessary environment to run this project and leverage its real-time and batch processing capabilities for smartphone price prediction and analysis.
## 6. How to Run
To set up and run the project locally, follow these steps:
- Clone the repository:
```bash
git clone https://github.com/aymane-maghouti/Big-Data-Project
```
#### **1. Stream Layer**
- Start Apache zookeeper
```batch
zookeeper-server-start.bat C:/kafka_2.13_2.6.0/config/zookeeper.properties
```
- Start Kafka server
```batch
kafka-server-start.bat C:/kafka_2.13_2.6.0/config/server.properties
```
- Create Kafka topic
```batch
kafka-topics.bat --create --topic smartphoneTopic --bootstrap-server localhost:9092
```
- Run the kafka producer
```batch
kafka-console-producer.bat --topic smartphoneTopic --bootstrap-server localhost:9092
```
- Run the kafka consumer
```batch
kafka-console-consumer.bat --topic smartphoneTopic --from-beginning --bootstrap-server localhost:9092
```
- Start HDFS and yarn (start-all or start-dfs and start-yarn)
```batch
start-all
```
- Start Hbase
```batch
start-hbase
```
- Run thrift server (for Hbase)
```batch
hbase thrift start
```
after all this run `stream_pipeline.py` script.
and then open the spring boot appliation in your idea and run it (you can access to the web app locally on `localhost:8081/`)
---
note that there is another version of the web app developed using Flask micro-framework(watch the demo video for mor details)
#### **2. Batch Layer**
- Start the Apache Airflow instance:
```batch
docker-compose up -d
```
Access the Apache Airflow web UI (localhost:8080) and run the DAG
- Start Apache Spark
```batch
spark-shell
```
- Start Apache zookeeper
```batch
zookeeper-server-start.bat C:/kafka_2.13_2.6.0/config/zookeeper.properties
```
- Start Kafka server
```batch
kafka-server-start.bat C:/kafka_2.13_2.6.0/config/server.properties
```
- Run the kafka producer
```batch
kafka-console-producer.bat --topic smartphoneTopic --bootstrap-server localhost:9092
```
- Run the kafka consumer
```batch
kafka-console-consumer.bat --topic smartphoneTopic --from-beginning --bootstrap-server localhost:9092
```
- Run HDFS and yarn (start-all or start-dfs and start-yarn)
```batch
start-all
```
- Open power BI file `dashboard.pbix` attached with this project
after all this run `syc_with_Airflow.py` script.
## 7. Dashboards
This project utilizes two dashboards to visualize smartphone price predictions and historical data:
#### **1. Real-Time Dashboard (Spring Boot Application):**
- This dashboard is built using a Spring Boot web application.
- It displays the **predicted price of smartphones in real-time**.
- Users can access this dashboard through a web interface.
Here is the UI of th Spring Boot web application:
#### **2. Batch Dashboard (Power BI):**
- This dashboard leverages Power BI for interactive data exploration.
- It provides insights into **historical smartphone price trends**.
- This dashboard is designed for batch users interested in historical analysis.
Here is the Dashboard created in Power BI:
## 8. Acknowledgments
- Special thanks to the open-source communities behind `Python`, `Kafka`, `HDFS` , `Spark`,`Hbase`,`Spring Boot`and `Airflow`
## 9. Conclusion
- This big data architecture effectively predicts smartphone prices in real-time and provides historical analysis capabilities. The Lambda architecture facilitates efficient stream processing for real-time predictions using XGBoost and HBase, while Apache Airflow orchestrates batch processing with Spark to populate the PostgreSQL data warehouse for historical insights. This solution empowers real-time and batch users with valuable price information, enabling data-driven decision-making.
you can watch the demo video here
## 10. Contacts
For any inquiries or further information, please contact:
- **Name:** Aymane Maghouti
- **Email:** [email protected]
- **LinkedIn:** Aymane Maghouti