Summary of "$100k+ Data Engineer Complete Roadmap 2024 🚀 Skills, Tools, Future Scope, Salary ✅"
Summary of the Video: $100k+ Data Engineer Complete Roadmap 2024
In this video, Sahil Gana, a data engineer at a top Canadian bank, shares insights on how to start a career in data engineering, emphasizing the importance of understanding fundamentals and developing a problem-solving mindset. He outlines the essential skills, tools, and methodologies needed to succeed in this field.
Main Ideas and Concepts:
- Mindset for Data Engineering:
- Emphasizes a problem-solving mindset, crucial for tackling daily challenges in data engineering.
- Encourages continuous learning and adaptability in a rapidly changing environment.
- Distinction Between Data Roles:
- Clarifies the differences between Data Engineers, Data Scientists, and Data Analysts.
- Essential Skills and Tools:
- Basics of Computer Science: Fundamental knowledge required for data engineering.
- Specific Data Engineering Skills: Tools and technologies specific to the field.
- Key Technologies and Languages:
- Fundamentals of Data Engineering:
- Data Warehousing: Understanding the difference between databases and data warehouses, and concepts like ETL (Extract, Transform, Load) and ER modeling.
- Big Data Ecosystem: Awareness of technologies like Hadoop and its components (e.g., MapReduce, HDFS).
- Data Processing Frameworks: Learning Apache Spark for in-memory data processing.
- Real-time Data Handling: Familiarity with pub/sub systems for streaming data.
- Data Orchestration Tools: Understanding tools like Apache Airflow for job scheduling and monitoring.
- Project Development:
- Importance of building end-to-end data pipelines using the learned tools and technologies.
- Suggests utilizing resources like Darshal Parmar’s videos for project ideas.
- Career Transition:
- Emphasizes that strong foundational knowledge and practical project experience are key to successfully transitioning into a data engineering role.
Methodology/Instructions:
- Step-by-step Learning Path:
- Start with SQL to understand data structures.
- Learn a programming language (preferably Python).
- Familiarize yourself with data warehousing concepts.
- Explore the Big Data ecosystem and its technologies.
- Gain proficiency in data processing frameworks like Spark.
- Understand real-time data handling and orchestration tools.
- Build projects that incorporate these skills to demonstrate competence.
Speakers/Sources Featured:
- Sahil Gana: Data engineer and presenter of the video.
- Tech TF: Recommended resource for learning SQL.
- Darshal Parmar: Suggested for project ideas and end-to-end pipeline projects.
Category
Educational
