Overview of Data Engineering
This comprehensive data engineering training program is designed for professionals who want to learn how to design, build, and maintain data infrastructure for various data science projects. The course focuses on providing students with the necessary skills to implement data pipelines and create data warehouses that can support machine learning and data analysis applications. The program emphasizes hands-on learning, and students will work with various tools and technologies to build real-world data engineering solutions.
The course covers essential data engineering concepts such as data modeling, data warehousing, data ingestion, data transformation, and data visualization. The curriculum also includes training on different data storage and processing technologies such as SQL and NoSQL databases, distributed computing platforms, and cloud-based data services. The program will also cover data security and data governance practices.
Stalwart Learning, an industry leader in providing top-notch data science training, has designed this program to cater to the needs of professionals who want to stay ahead of the curve and gain expertise in the data engineering domain.
Key Topics Covered:
- Introduction to Data Engineering
- Data Modeling and Data Warehousing
- Data Ingestion and Data Transformation
- Data Storage and Processing Technologies
- Cloud-Based Data Services
- Data Security and Governance
- Who Should Attend:
- Data Analysts and Scientists
- Business Intelligence Analysts
- Database Administrators
- IT Professionals
- Software Developers
- Data Engineers
By the end of the program, students will have gained in-depth knowledge and hands-on experience in building data pipelines, designing data warehouses, and working with different data storage and processing technologies. The program also provides a comprehensive understanding of data governance and security, which are critical aspects of any data engineering project.
Duration
40 Hours
Module 1: Introduction to Data Engineering
- Overview of data engineering and its importance in modern organizations
- Understanding the data engineering workflow and process
- Introduction to key concepts and technologies in data engineering
- Overview of data storage, processing, and integration
Module 2: Relational Databases and SQL
- Introduction to relational databases and SQL
- Creating and managing relational database tables
- Querying and manipulating data with SQL
- Advanced SQL techniques for data engineering
Module 3: Data Modeling and Design
- Introduction to data modeling concepts
- Entity-relationship (ER) modeling and normalization
- Designing database schemas for efficient data storage and retrieval
- Best practices for data modeling in data engineering
Module 4: Big Data Technologies
- Introduction to big data concepts and technologies
- Overview of Apache Hadoop and HDFS
- Introduction to Apache Spark for distributed data processing
- Working with distributed file systems and data processing frameworks
Module 5: Data Warehousing and ETL
- Introduction to data warehousing concepts
- Designing and building data warehouses
- Extract, Transform, Load (ETL) processes and techniques
- Data integration and transformation with ETL tools
Module 6: Data Pipelines and Workflow Management
- Introduction to data pipelines and workflow management
- Building and orchestrating data pipelines
- Workflow management tools and frameworks
- Monitoring and managing data pipelines
Module 7: Data Integration and Streaming
- Introduction to data integration and streaming
- Real-time data ingestion and processing techniques
- Streaming data pipelines with Apache Kafka and Apache Flink
- Managing and processing streaming data
Module 8: Data Quality and Governance
- Introduction to data quality and data governance
- Ensuring data integrity and quality in data engineering pipelines
- Implementing data governance practices and policies
- Data privacy and security in data engineering
Module 9: Cloud Data Engineering
- Introduction to cloud computing and its role in data engineering
- Cloud-based data storage and processing platforms
- Building scalable and cost-effective data pipelines in the cloud
- Data engineering best practices in cloud environments
Module 10: Advanced Data Engineering Concepts
- Advanced data processing and analytics techniques
- NoSQL databases and their use cases
- Data engineering for machine learning and AI applications
- Emerging trends and developments in data engineering