Data Engineer: Road Map
Road Map documentations.
Learning Path:
Database Knowledge: Learn about different types of databases (SQL, NoSQL), and gain proficiency in SQL.
ETL Process: Learn about ETL tools like Apache NiFi, Talend, etc., and become proficient in designing and implementing ETL processes.
Big Data Technologies: Gain knowledge on Big Data technologies like Hadoop, Spark, etc.
Cloud Platforms: Learn about cloud platforms like AWS, Azure, or GCP and their data services.
Programming & Scripting: Strengthen your Python skills and learn other scripting languages if possible.
Resources:
Online Learning Platforms like Coursera, Udemy, and edX offer courses on the above-mentioned technologies and concepts.
Leverage documentation and tutorials available on the official websites of the technologies you are learning.
Use Cases:
Real-time Data Processing: Design a system to process and analyze real-time streaming data, e.g., from social media or IoT devices.
Data Warehouse Migration: Migrate an existing Data Warehouse to a cloud platform and optimize for performance and cost.
Log Analysis: Analyze log files to extract insights or detect anomalies.
ETL Workflow Design: Design an ETL workflow to ingest, transform, and load data from various sources into a Data Warehouse.
Data Lake Implementation: Implement a Data Lake to store structured and unstructured data and make it available for analysis.
Remember to create projects utilizing these use cases and add them to your portfolio on GitHub, as having practical experience will significantly increase your chances of securing a data engineering position. Lastly, tailor your resume to highlight relevant skills, experiences, and projects related to data engineering. Good luck with your transition!
Last updated