Data Engineering Course Overview:
Data Engineering refers to the process of designing, building, and maintaining systems that enable the collection, storage, analysis, and processing of large volumes of data.
The Data Engineering course is a comprehensive project-based program that covers the fundamentals as well as the advanced knowledge areas encompassing all the aspects of data engineering. Starting from the basics, the course gradually progresses to cover advanced data engineering concepts and techniques. It covers data engineering fundamentals, data modeling, integration, big data, and cloud computing.
At the end of the program, participants will engage in hands-on projects and practical exercises to reinforce their learning and gain real-world experience.
Data Engineering Course Outline:
Module 1: Introduction to Data Engineering
- Understanding the role of data engineering in the data ecosystem
- Overview of data engineering tools, technologies, and job roles
- Exploring the key concepts and components of data engineering
Module 2: Relational Databases and SQL
- Understanding relational databases and their importance in data engineering
- Fundamentals of SQL (Structured Query Language)
- Designing and querying databases using SQL
Module 3: Data Modeling and Design
- Principles of data modeling and schema design
- Implementing entity-relationship diagrams (ERDs)
- Normalization techniques and best practices
Module 4: Data Integration and ETL
- Extract, Transform, Load (ETL) processes and their significance
- Techniques for data extraction from diverse sources
- Data transformation and cleansing using ETL tools
Module 5: Big Data Fundamentals
- Introduction to big data concepts and challenges
- Understanding distributed computing frameworks (e.g., Hadoop, Spark)
- Processing large-scale data using Hadoop and MapReduce
Module 6: Stream Processing and Real-time Analytics
- Exploring stream processing concepts and frameworks
- Building real-time data pipelines for streaming data
- Performing real-time analytics and event-driven processing
Module 7: Data Warehousing and Dimensional Modeling
- Understanding data warehousing concepts and architectures
- Designing and implementing data warehouses
- Dimensional modeling techniques for data analysis and reporting
Module 8: Cloud Computing for Data Engineering
- Introduction to cloud computing and its relevance to data engineering
- Leveraging cloud platforms for data storage and processing
- Deploying scalable data engineering solutions on the cloud
Module 9: Data Orchestration and Workflow Management
- Overview of workflow management tools (e.g., Airflow, Luigi)
- Designing and scheduling complex data workflows
- Monitoring and error handling in data engineering pipelines
Module 10: Advanced Data Engineering Techniques
- Advanced data processing and optimization techniques
- Implementing data caching, indexing, and partitioning strategies
- Performance tuning and troubleshooting data pipelines
- Become Job Ready
- Become an expert in the field.
- Increase earning potential.
I am Deborah Osilade, I am a Data Analyst and an Instructor at Piston and Fusion Business Academy. I love leveraging data to create high-quality data-intelligent solutions. I enjoy working on building Predictive Models, Financial models and designing Dashboards solutions. My specialization and core skills are in Finance, Data Science & Machine Learning. I have a Post Graduate Diploma in Data Science and Business Analysis from the University of Texas at Austin and a B.A. Accounting and Finance degree from Middlesex University. My proficiency with data science tools spans Programming Language for Data Science, Data visualization, and Data management tools.
Data Engineering Course Fees & Dates for Upcoming Classroom Classes In 2024
| Batch 1 | Batch 2 | |
| Date | Contact Us | Contact Us |
| Fees | ₦350,000 | ₦350,000 |
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