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Data Engineering Journey

Embarking on a new journey always brings a sense of excitement and challenge. Today, I am thrilled to begin my journey into the world of data engineering. This journey will be marked by learning, experimentation, and the application of cutting-edge tools and techniques to transform raw data into actionable insights

Introduction

Data engineering is the backbone of modern data-driven decision-making. It involves designing, building, and maintaining systems and architectures that enable the collection, storage, processing, and analysis of large volumes of data. As businesses increasingly rely on data to drive their strategies and operations, the role of a data engineer has become pivotal.

My project will integrate various tools and technologies fundamental to data engineering, including Docker for containerization, Terraform for infrastructure provisioning, Airflow for workflow orchestration, data warehouses for structured storage, dbt for data transformation, Apache Spark for batch processing, and Apache Kafka for stream processing. This comprehensive approach will help me build a robust data pipeline, providing a solid foundation for my career in data engineering.

Objective

The primary objective of this project is to design, build, and integrate a complete data pipeline using industry-standard tools and frameworks. By the end of this journey, I aim to achieve the following:

  1. Proficiency in Docker: Learn to containerize applications and manage containers efficiently.
  2. Infrastructure as Code with Terraform: Automate the provisioning and management of infrastructure.
  3. Workflow Orchestration with Airflow: Create and manage data workflows to ensure seamless data processing.
  4. Data Warehousing: Setup and manage a data warehouse to store structured data.
  5. Analytics Engineering with dbt: Transform raw data into clean, analyzed data ready for insights.
  6. Batch Processing with Apache Spark: Handle large-scale data processing in batch mode.
  7. Stream Processing with Apache Kafka: Process real-time data streams effectively.

This project marks the beginning of my commitment to mastering data engineering, with a focus on continuous learning and practical application. By working on this project after work hours, I plan to gradually build my expertise and contribute meaningfully to the field of data engineering.

Prerequisites

  • Docker
  • Terraform
  • Airflow or Prefect
  • A data warehouse (BigQuery, Redshift, Snowflake, etc.)
  • dbt (Data Build Tool)
  • Apache Spark
  • Apache Kafka

Setup Instructions

Docker

  1. Install Docker: Docker Installation Guide
  2. Build the Docker container: 
    docker build -t my_project .
  3. Run the Docker container: 
    docker run -d -p 8080:8080 my_project
  4. Terraform
    1. install Terraform Follow the Terraform Installation Guide to install Terraform on your system.
    2. Initialize Terraform Navigate to the 'terraform/' directory and initialize Terraform: 
      terraform init
    3. Apply Terraform Scripts Apply the Terraform scripts to provision the infrastruktur: 
      terraform apply

Workflow Orchestration

  1. Install Airflow or Prefect
  2. Define and Deploy Workflows

Data Warehouse