Development

Data Engineering Bootcamp

Dive deep into the world of Data engineering with our Ultimate Data Engineering BootCamp. Master essential skills in data manipulation, analysis, and visualization using tools like Excel, SQL, Python, and PowerBI. Learn to clean, process, and analyze data to make informed decisions and drive business insights. Elevate your data expertise and take the first step toward a successful career in engineering!
4.7 (253 user ratings)
Rated 4.7 out of 5
  • 4.721 students
  • Last updated 1/2025

Data Engineering Development: Course Outline

  • Objective: Master the primary languages and storage systems used to move and query data.

  • Python for DE: Data structures, file handling, and working with APIs.

  • Advanced SQL: Complex joins, window functions, and query optimization.

  • Relational Databases (RDBMS): Designing schemas in PostgreSQL or MySQL.

  • NoSQL Systems: Understanding MongoDB and Cassandra for unstructured data.

  • Objective: Understand how data flows through an organization and how it differs from other fields.

  • The Lifecycle: Data collection, cleaning, transformation, and delivery.

  • DE vs. Related Fields: Distinguishing the DE role (building infrastructure) from Data Analysts (dashboards) and DevOps (system reliability).

  • Data Architecture: Introduction to Data Lakes (S3, Azure Data Lake) vs. Data Warehouses.

  • Objective: Learn to automate the movement and transformation of data.

  • ETL vs. ELT: Understanding Extract, Transform, and Load methodologies.

  • Pipeline Orchestration: Using Apache Airflow to schedule and monitor workflows.

  • Data Transformation: Using dbt (data build tool) for modular SQL transformations.

  • Objective: Deploy data solutions on modern cloud infrastructure.

  • Cloud Providers: Specialized training in AWS, Azure, or GCP.

  • Modern Warehousing: Scaling storage and compute with Snowflake, BigQuery, or Redshift.

  • Storage Optimization: Managing S3 buckets and GCS for scalable data lakes.

  •  
  • Objective: Handle massive datasets and streaming data in real-time.
  • Distributed Computing: Processing large-scale data with Apache Spark

  • Stream Processing: Handling real-time data feeds with Apache Kafka.

  • Scaling Systems: Reducing latency and maintaining performance as data grows.

  • Objective: Build a professional portfolio and prepare for the DE job market.

  • Portfolio Projects: * Build a complete end-to-end ETL Pipeline.

  • Deploy a real-time streaming application on the Cloud.

  • Soft Skills: Collaboration with Data Scientists and Analysts and problem-solving.

  • Recommended Reading: Study "Designing Data-Intensive Applications" by Martin Kleppmann.

Instructors
Awais Akram

Elite Data Engineer

This course includes:
  • Level: Begineer to Professional
  • Class Timings: 8:00pm-9:00pm
  • Duration: 6 Months
  • Days: Mon-Tue-Wed
  • Course Certificate
  • Lifetime Support
  • Free Trail Session
Enroll now
share it :