README: Data Engineering

Welcome to the Data Engineering repository! This repository contains materials, examples, and resources to help you understand and implement various aspects of data engineering, including ETL (Extract, Transform, Load) pipelines, orchestration, scheduling, SFTP, and building dashboards.

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Table of Contents

1. Introduction to Data Engineering
2. ETL Pipelines
3. Orchestration and Scheduling
4. Sections and Resources
   • Data Extraction
   • Data Transformation
   • Data Loading
   • SFTP Integration
   • ETL Pipeline Orchestrator
   • Streamlit Dashboard
5. Infrastructure
6. How to Use This Repository

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Introduction to Data Engineering

Data engineering focuses on designing, building, and maintaining systems that enable the collection, storage, and analysis of data. It is a critical field that supports data-driven decision-making in organizations.

Key concepts in data engineering include:

• ETL Pipelines: The process of extracting data from various sources, transforming it into a usable format, and loading it into a target system (e.g., a database or data warehouse).
• Orchestration and Scheduling: Automating and managing workflows to ensure data pipelines run reliably and on schedule.
• SFTP Integration: Securely transferring files between systems as part of the data pipeline.
• Dashboards: Visualizing data insights using tools like Streamlit.

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ETL Pipelines

ETL (Extract, Transform, Load) is a core process in data engineering. It involves:

1. Extract: Retrieving data from various sources such as APIs, databases, or flat files.
2. Transform: Cleaning, enriching, and converting the data into a usable format.
3. Load: Storing the transformed data into a target system, such as a data warehouse or database.

This repository provides examples and resources for building ETL pipelines using modern tools and frameworks.

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Orchestration and Scheduling

Orchestration and scheduling are essential for automating and managing data workflows. This repository includes examples of:

• Workflow Orchestration: Using tools like Apache Airflow or Prefect to manage complex ETL pipelines.
• Scheduling: Automating pipeline execution using cron jobs or orchestration tools.
• Error Handling: Ensuring workflows are robust and can recover from failures.

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Sections and Resources

1. Data Extraction

This section covers techniques and tools for extracting data from various sources, including:

• APIs
• Relational databases
• Flat files (e.g., CSV, JSON)

2. Data Transformation

Learn how to clean, enrich, and transform raw data into a structured format. Topics include:

• Data cleaning with Python (e.g., Pandas)
• Data validation techniques

3. Data Loading

Explore methods for loading transformed data into target systems, such as:

• Relational databases (e.g., PostgreSQL, MySQL)
• Data warehouses (e.g., Snowflake, BigQuery)
• Cloud storage (e.g., AWS S3, Azure Blob Storage)

4. SFTP Integration

This section demonstrates how to securely transfer files between systems using SFTP. Topics include:

• Setting up an SFTP server
• Automating file transfers
• Integrating SFTP into ETL pipelines

5. ETL Pipeline Orchestrator

Learn how to orchestrate and schedule ETL pipelines using different tools. Topics include:

• Python ETL: Basic programmatic pipeline
  • Python ETL with AWS
• Apache Airflow: A powerful workflow orchestration tool.
• Cron Jobs: Lightweight scheduling for simple pipelines.

6. Streamlit Dashboard

Build interactive dashboards to visualize data insights. Topics include:

• Setting up a Streamlit app
• Connecting the dashboard to ETL pipelines
• Visualizing data with charts and tables

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Infrastructure (Docker and Terraform)

This section focuses on infrastructure tools like Docker and Terraform, which are essential for automating and managing data engineering workflows. Note: This section is a work in progress.

1. Docker

Docker is a platform that allows you to package applications and their dependencies into lightweight, portable containers. It is widely used in data engineering for:

• Running ETL pipelines in isolated environments.
• Deploying workflow orchestration tools like Apache Airflow.
• Managing dependencies for data processing frameworks.

Topics covered:

• Setting up a Dockerfile
• Building and running Docker containers
• Using Docker Compose for multi-container setups

2. Terraform

Terraform is an Infrastructure as Code (IaC) tool that allows you to define and provision cloud resources in a declarative way. It is useful for:

• Automating the creation of data lakes, data warehouses, and other cloud resources.
• Managing infrastructure for ETL pipelines.
• Scaling resources for large-scale data processing.

Topics covered:

• Basic Terraform setup
• Provisioning cloud resources (e.g., AWS S3, Redshift)
• Managing infrastructure as code

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How to Use This Repository

1. Clone the Repository:

   git clone https://github.com/your-username/Data-Engineering.git
   cd Data-Engineering

2. Explore Sections: Navigate to the relevant folders for specific topics:

   • Data Extraction
   • Data Transformation
   • Data Loading
   • SFTP Integration
   • ETL Pipeline Orchestrator
   • Streamlit Dashboard

3. Run Examples: Follow the instructions in each section to run the provided examples and scripts.

4. Contribute: Feel free to contribute to this repository by submitting pull requests or opening issues.