Spark Data Engineering

Welcome to the Spark Data Engineering repository! This project demonstrates the use of Apache Spark for real-time and batch data processing, providing insights into how data engineering, data analytics, and data science roles interplay within the data ecosystem.

Overview

This repository is a hands-on guide for building scalable data pipelines using Apache Spark. It covers essential concepts such as data acquisition, transport, storage, processing, and serving, with examples of both batch and real-time data processing.

Key Sections:

• Data Engineer vs Data Analytics vs Data Science
• Data Engineer Functions
• Batch vs Real-Time Processing
• Data Engineering with Spark

Data Engineer vs Data Analytics vs Data Science

Data Engineering, Data Analytics, and Data Science are interconnected yet distinct roles in the data ecosystem. Here’s how they differ:

• Data Engineers build and maintain the infrastructure for data collection, storage, and processing.
• Data Analysts interpret data to provide actionable insights.
• Data Scientists develop predictive models and algorithms to uncover deeper insights from data.

Data Engineer Function

1. Acquisition

Extract data from various sources (databases, APIs, etc.) and publish it to a data pipeline.

2. Data Transport

Move data within the pipeline securely and efficiently.

3. Data Storage

Manage data storage with a focus on latency, security, and scalability.

4. Data Processing

Cleanse, filter, and transform data to prepare it for analysis.

5. Data Serving

Make processed data available for consumption with security in mind, using pull or push models.

Batch vs Real-Time Processing

Batch Processing

Processes data in large volumes at scheduled intervals, ideal for tasks like reporting and data aggregation.

Real-Time Processing

Processes data continuously as it arrives, essential for applications requiring immediate analysis and action.

Data Engineering with Spark

Apache Spark is a powerful tool for both batch and real-time data processing. This section demonstrates how to use Spark for various data engineering tasks:

• Data Ingestion: Ingest data from sources like HDFS, Kafka, etc.
• Data Transformation: Perform complex data transformations using Spark’s API.
• Batch Processing: Efficiently process large datasets with Spark’s RDDs.
• Real-Time Processing: Utilize Spark Streaming for real-time data processing.
• Data Storage and Serving: Integrate with various storage systems to serve processed data.

Benefits of Using Spark

• Speed: In-memory processing for faster data handling.
• Ease of Use: Supports multiple programming languages (Java, Scala, Python, R).
• Scalability: Handles large datasets with ease.
• Unified Engine: Supports both batch and stream processing.

Getting Started

Prerequisites

• Apache Spark 3.x
• Java 8 or higher
• Scala 2.12 or higher
• Hadoop 3.x (for HDFS integration)

Installation

1. Clone the repository: ```bash git clone https://github.com/rishabh-agarwal/spark-data-engg.git ```
2. Navigate to the project directory: ```bash cd spark-data-engg ```
3. Set up your Spark environment and install necessary dependencies.

Running Examples

1. Batch Processing Example:
   • Follow the instructions in the batch-processing/ directory to run batch processing jobs.
2. Real-Time Processing Example:
   • Navigate to the real-time-processing/ directory for real-time processing examples using Spark Streaming.

Additional Resources

• Apache Spark Documentation
• Kafka Integration Guide
• HDFS Integration Guide

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.