Introduction
This roadmap explains the steps for learning data engineering from zero. The goal is to build strong skills in Python, SQL, pipelines, big data, and cloud systems. Next, you move from basic programming to real projects.
هاد ال roadmap كتعطي طريق واضح باش تبدا ف data engineering. غادي تمشي من Python حتى ETL و cloud.
1. Learn Programming Basics
Python is the core language in most data engineering workflows. Build clean scripts and understand how to manage files and simple APIs.
Key Skills
- Variables and data structures
- Functions
- File handling
- APIs basics
- Error handling
تعلم Python basics. خدم ب functions و file handling و APIs بسيطة.
2. Learn SQL Deeply
SQL drives data extraction and transformation. Learn how queries work and how indexes improve performance.
Focus Points
- Select queries
- Joins
- Window functions
- Aggregation
- Indexes
تعلم SQL مزيان. SELECT و JOIN و window functions مهمين بزاف.
3. Learn Databases
Understand how databases store structured and unstructured data. Learn both relational and non relational systems.
Systems to Study
- MySQL
- PostgreSQL
- MongoDB
- Cassandra
تعلم MySQL و Postgres و MongoDB باش تفهم storage methods مختلفة.
4. Learn Data Modeling
Data modeling helps design clean schemas and stable structures for analytics and pipelines.
Core Concepts
- Normalization
- Star schema
- Snowflake schema
- Primary keys and foreign keys
فهم normalization و star schema باش تبني جداول مرتبة.
5. Learn ETL and ELT
ETL and ELT form the heart of data pipelines. Learn how to extract, transform, and load data at scale.
Skills to Build
- Batch processing
- Streaming basics
- Scheduling jobs
- Data cleaning
تعلم batch و streaming و scheduling باش تنظم البروسيس كامل.
6. Learn Data Warehousing
Warehouses store large datasets for analytics. They support business intelligence and reporting.
Technologies
- Snowflake
- BigQuery
- Redshift
- Databricks Lakehouse
جرب Snowflake ولا BigQuery باش تفهم data warehousing.
7. Learn Big Data Tools
Big data tools process large volumes efficiently. Learn distributed systems and streaming technologies.
Tools to Study
- Hadoop basics
- Spark
- Kafka
- Flink
تعلم Spark و Kafka باش تخدم على data كبيرة.
8. Learn Cloud Platforms
Most pipelines run on cloud services. Learn one provider and understand the core services used for storage and compute.
Cloud Providers
- AWS
- GCP
- Azure
Important Services
- Storage buckets
- Serverless compute
- Managed databases
- Workflow tools
تعلم services بحال storage buckets و serverless compute.
9. Learn Orchestration Tools
Orchestration tools help manage data workflows. Learn how to automate and monitor pipelines.
Popular Tools
- Airflow
- Prefect
- Dagster
جرب Airflow و Prefect باش تدير scheduling و monitoring.
10. Build Real Projects
Projects build strong skills. Work with ETL, warehouses, and cloud workflows.
Project Ideas
- ETL pipeline with Airflow
- Data warehouse for sales data
- Spark batch processing app
- Kafka streaming pipeline
دير مشاريع بحال ETL pipeline ولا streaming باش تولي محترف.
Syntax or Workflow Example
Below is a simple Python example for loading JSON and writing it into a CSV.
import json
import pandas as pd
with open("data.json") as f:
rows = json.load(f)
df = pd.DataFrame(rows)
df.to_csv("output.csv", index=False)
print("Saved CSV")
هادا مثال بسيط باش تحول JSON ل CSV.
Exercises
- Write a SQL query using a window function.
- Create a small schema with a star design.
- Build a simple ETL script that cleans and loads data.
- Run a Spark job on sample data.
- Create a Kafka topic and publish messages.
- Write a script to load data into BigQuery or Snowflake.
- Build a DAG in Airflow.
- Design a small warehouse for analytics.
- Create a simple cloud storage bucket.
- Schedule a daily data pipeline.
Conclusion
Follow the steps and keep practicing. Data engineering grows with clean pipelines, clear modeling, and strong understanding of tools.
تبع الخطوات و خدم مزيان باش تولي data engineer قوي.