Data engineering roadmap

1. Basic Understanding of Data Science:

•Learn about data types, data structures, and basic data analysis techniques.

•Explore introductory resources such as online tutorials, books, or courses like Coursera's "Introduction to Data Science."

2. Programming Foundations:

•Start with Python as it's widely used in data engineering for its versatility and ease of use.

•Learn the basics of Python syntax, data types, control flow, and functions.

•Practice coding through online platforms like Codecademy or LeetCode.

3. SQL Proficiency:

•Understand the fundamentals of SQL including querying, filtering, joining tables, and aggregating data.

•Practice SQL queries using platforms like SQLZoo, Mode Analytics, or PostgreSQL tutorial.

4. Data Warehousing Concepts:

•Study concepts such as data warehousing architecture, ETL processes, data modeling (e.g., star schema, snowflake schema), and data normalization.

•Resources like "The Data Warehouse Toolkit" by Ralph Kimball provide comprehensive coverage of these topics.

5. Big Data Technologies:

•Learn about Hadoop ecosystem components (HDFS, MapReduce, Hive, Pig) and Apache Spark for distributed data processing.

•Resources like "Hadoop: The Definitive Guide" by Tom White and online courses from platforms like Udemy or Pluralsight can help.

6. Cloud Platforms:

•Familiarize yourself with cloud platforms such as AWS (Amazon Web Services), GCP (Google Cloud Platform), or Azure (Microsoft Azure).

•Start with basic services like storage (S3, Google Cloud Storage, Azure Blob Storage) and compute (EC2, Google Compute Engine, Azure Virtual Machines).

7. Data Pipeline Tools:

•Explore workflow management tools like Apache Airflow, Apache NiFi, or Luigi for orchestrating data pipelines.

•Set up simple data pipelines to understand workflow scheduling, dependencies, and monitoring.

8. Project-based Learning:

•Work on projects that involve real-world data engineering tasks such as data ingestion, transformation, and loading into a data warehouse.

•Start with small projects and gradually increase complexity as you gain experience.

9. Continuous Learning and Community Engagement:

•Stay updated on the latest trends and technologies by following blogs, attending conferences, and participating in online communities like Stack Overflow or Reddit.

•Engage in discussions, ask questions, and share your knowledge to learn from others in the field.

10. Build a Portfolio and Specialize:

•Showcase your projects and skills through a portfolio on platforms like GitHub or a personal website.

•Consider specializing in specific areas of data engineering such as streaming data processing, data integration, or machine learning infrastructure, based on your interests and career goals.

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