Monday, 22 January 2024
Roadmap for Data Engineer(From scratch)
1. Programming Language - Python:
● Acquire proficiency in Python fundamentals,including variables, data types
etc.
● Understand functions, modules, and packages in Python.
● Explore the principles of object-oriented programming (OOP).
● Familiarize yourself with Python libraries essential for data engineering, such
as Pandas, NumPy, and requests.
2. SQL:
● Master the basics of SQL, covering essential commands like SELECT,
FROM, WHERE, GROUP BY, and JOIN.
● Learn advanced SQL concepts such as subqueries, window functions, and
indexing.
● Understand the principles of database design and normalization.
● Practice writing intricate queries and optimizing them for performance.
3. Read the Book - "Fundamentals of Data Engineering":
● Gain a comprehensive understanding of data engineering concepts and best
practices from the book.
● Apply the learned principles to real-world scenarios.
TOOLS:
4. Data Warehouse (Snowflake):
● Comprehend the concept of data warehousing.
● Learn about Snowflake's architecture and features.
● Gain practical experience by creating databases, tables, and querying data in
Snowflake.
5. Data Processing (Spark, Databricks):
● Explore Apache Spark and its ecosystem.
● Master Spark's core concepts, including RDDs, DataFrames, and Datasets.
● Experiment with Spark using Databricks, a cloud-based collaborative
environment.
6. Apache Spark:
● Deepen your knowledge of Spark by exploring its machine learning (MLlib)
and graph processing (GraphX) libraries.
● Understand Spark optimization techniques for effective performance tuning.
7. Kafka:
● Grasp the fundamentals of Apache Kafka for constructing real-time data
pipelines.
● Learn about Kafka topics, producers, and consumers.
● Understand Kafka's role in event-driven architectures.
8. Orchestration Tool (Airflow):
● Explore Apache Airflow for workflow orchestration and automation.
● Learn to create, schedule, and monitor workflows.
● Understand the concepts of DAGs (Directed Acyclic Graphs) in Airflow.
9. Cloud (GCP/AWS):
● Choose a cloud provider (Google Cloud Platform - GCP or Amazon Web
Services - AWS).
● Explore cloud services for data engineering, storage, and processing.
● Practice deploying and managing data pipelines in the chosen cloud
environment.
10. Update Yourself in Open Table Format (Iceberg):
● Stay abreast of emerging technologies and tools, such as Iceberg for
managing large datasets.
● Follow community forums, blogs, and official documentation for updates.
● Experiment with new tools and frameworks to broaden your skill set.
General Tips:
● Engage in hands-on projects to practically apply your knowledge.
● Participate in online communities and forums to connect with the data
engineering community.
● Attend webinars, workshops, and conferences to stay current with industry
trends.
● Consider pursuing certifications in relevant tools and cloud platforms to
validate your skills.
This roadmap is designed to provide a structured path for individuals starting from
scratch to become proficient Data Engineers. Tailor the pace of your learning based
on your style and goals. Best of luck on your data engineering journey!