77 Languages
Logo
Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages

Data Engineering
( 24 Modules )

Module #1
Introduction to Data Engineering
Overview of data engineering, importance, and role in data science
Module #2
Data Engineering Fundamentals
Data processing, data storage, and data architecture basics
Module #3
Data Ingestion Patterns
Designing and implementing data ingestion pipelines
Module #4
Data Storage Options
Relational databases, NoSQL databases, and data warehousing
Module #5
Big Data Storage Solutions
HDFS, HBase, and other big data storage options
Module #6
Data Processing Fundamentals
Batch processing, stream processing, and real-time processing
Module #7
Apache Spark Fundamentals
Introduction to Apache Spark, Spark Core, and Spark SQL
Module #8
Batch Processing with Apache Spark
Batch processing use cases and implementations with Spark
Module #9
Stream Processing with Apache Spark
Stream processing use cases and implementations with Spark
Module #10
Real-Time Processing with Apache Flink
Introduction to Apache Flink and real-time processing use cases
Module #11
Data Pipelines and Workflow Management
Designing and implementing data pipelines with Apache Airflow and Apache NiFi
Module #12
Data Quality and Data Governance
Data quality metrics, data governance, and data lineage
Module #13
Data Security and Access Control
Data encryption, access control, and security best practices
Module #14
Cloud-Based Data Engineering
Cloud-based data engineering with AWS, GCP, and Azure
Module #15
Containerization and Orchestration
Containerization with Docker and orchestration with Kubernetes
Module #16
Monitoring and Logging in Data Engineering
Monitoring and logging best practices in data engineering
Module #17
Testing and Validation in Data Engineering
Testing and validation strategies for data pipelines and systems
Module #18
Data Engineering for Machine Learning
Data engineering for machine learning models and AI applications
Module #19
Real-World Data Engineering Use Cases
Case studies and real-world examples of data engineering applications
Module #20
Data Engineering Tools and Technologies
Survey of data engineering tools and technologies
Module #21
Data Engineering Best Practices
Best practices for data engineering design, development, and deployment
Module #22
Data Engineering at Scale
Scalability and performance considerations for large-scale data engineering systems
Module #23
Data Engineering for Data Science
Collaboration between data engineers and data scientists
Module #24
Course Wrap-Up & Conclusion
Planning next steps in Data Engineering career


Language Learning Assistant
with Voice Support

Hello! Ready to begin? Let's test your microphone.
  • Logo
Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!
We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

Copyright 2024 @ WIZAPE.com
All Rights Reserved
CONTACT-USPRIVACY POLICY