02.Data Engineer Road Map (Python, SQL, Spark & Databricks)

Data Engineer Road Map Content 😊
Python
SQL (Structured Query Language)
Spark (PySpark and Spark-SQL)
Databricks

Python
Python is a versatile, high-level programming language known for its simplicity and readability. It's widely used for a variety of applications, including web development, data analysis, artificial intelligence, and automation. Its extensive libraries and frameworks, such as Pandas and NumPy, make it a popular choice for both beginners and experienced developers.

SQL (Structured Query Language)
SQL is a domain-specific language used for managing and querying relational databases. It allows users to perform operations like querying, updating, and managing data within a database. SQL is essential for handling structured data and is a fundamental tool in data analysis, database management, and reporting.

PySpark
PySpark is the Python API for Apache Spark, a powerful distributed computing system. It enables Python developers to process large-scale data efficiently by leveraging Spark's capabilities. PySpark supports operations on distributed datasets, including transformations and actions, and provides tools for data processing, machine learning, and real-time analytics.

Spark SQL
Spark SQL is a component of Apache Spark that enables querying structured data using SQL as well as the DataFrame API. It integrates with various data sources and provides powerful querying and data manipulation capabilities. Spark SQL optimizes queries for performance and supports both batch and stream processing.

Databricks
Databricks is a cloud-based data platform that provides a collaborative environment for big data and machine learning workflows. It simplifies the management of Apache Spark clusters and integrates with cloud storage and data sources. Databricks offers interactive notebooks, advanced analytics tools, and a unified workspace for data engineers, data scientists, and analysts.