Description: Python is a high-level, versatile programming language used for scripting, automation, and building data pipelines.
Purpose: It’s the foundation of data engineering tasks, enabling fast development and integration.
Job Relevance: Strong Python skills are expected in every data engineering job.
Description: Pandas is a Python library for data analysis and manipulation using DataFrames.
Purpose: It helps clean, transform, and explore structured data efficiently.
Job Relevance: Essential for pre-processing data before loading into storage or models.
Description: Learn SQL, relational databases (PostgreSQL), and NoSQL databases (MongoDB, DynamoDB).
Purpose: Understand how to query, store, and manage structured data.
Job Relevance: Every data pipeline interacts with databases—this is a must-have skill.
Description: Extract data from websites using BeautifulSoup and requests.
Purpose: Used when data isn’t available via APIs.
Job Relevance: Useful for building POCs and extracting niche data from public sources.
Description: Learn AWS services like S3, Lambda, IAM, Glue, and CloudWatch.
Purpose: Cloud-based pipelines are scalable, secure, and fast.
Job Relevance: Cloud skills (especially AWS) are now mandatory in most data engineering jobs.
Description: Tools like AWS Glue or Airflow for extracting, transforming, and loading data.
Purpose: Automate and schedule data workflows across systems.
Job Relevance: Core part of every data engineer’s responsibilities.
Description: Centralized data storage using Redshift, Snowflake, etc.
Purpose: Organize and prepare data for analytics and dashboards.
Job Relevance: Important for enabling business intelligence and reporting teams.
Description: Work with REST APIs using Python for data exchange.
Purpose: Integrate external data sources into pipelines.
Job Relevance: Real-world pipelines often need API data ingestion.
Description: Process data in batches or real-time using tools like Kafka or Kinesis.
Purpose: Handle large or streaming datasets effectively.
Job Relevance: Required for companies with high data velocity (e.g., finance, e-commerce).
Description: Apply checks and validations to ensure clean and reliable data.
Purpose: Prevent bad data from flowing downstream.
Job Relevance: High-quality data pipelines reduce business risks.
Description: Use Python or BI tools to visualize and interpret data.
Purpose: Quickly identify trends, errors, and insights.
Job Relevance: Helps in data storytelling and debugging outputs.
Description: Tools like Git, Docker, and CI/CD pipelines.
Purpose: Automate testing and deployment of data workflows.
Job Relevance: DevOps knowledge is a major plus in modern engineering roles.
Description: Apache Spark’s Python API for large-scale distributed processing.
Purpose: Handle big data that doesn’t fit into memory.
Job Relevance: Widely used in big data pipelines in large enterprises.
Description: Use tools like Jira, Confluence, and Airflow UI for task tracking and orchestration.
Purpose: Helps you manage and track large projects and team efforts.
Job Relevance: Shows you're job-ready for enterprise workflows.
Description: Practice real-world Python, SQL, and cloud-based scenario questions.
Purpose: Build confidence in communication and technical reasoning.
Job Relevance: Helps you clear interviews and stand out among other candidates.
Description: Learn how to build a project-focused, skill-rich resume.
Purpose: Show your potential to recruiters effectively.
Job Relevance: A great resume increases your chances of shortlisting by 70%.
Description: You’ve completed the journey!
Purpose: You’re now job-ready to apply for Data Engineer roles.
Job Relevance: Equipped with all the skills to land a job in the data field.