• Collecting data from multiple sources
• Cleaning and transforming raw data
• Designing data pipelines (ETL/ELT processes)
• Building and maintaining data warehouses and data lakes
• Ensuring data quality, security, and scalability

• Secure
• Scalable
• Easily retrievable
• Optimized for performance

• Conceptual Data Model
• Logical Data Model
• Physical Data Model
• Common Data Modeling Techniques

• Workflow Scheduling
• Dependency Management
• Error Handling & Retry Mechanisms
• Monitoring & Logging
• Scalability & Resource Management

• What is clustering?
• Difference between K-means and KNN
• Different Use cases of clustering
• Building a model using K-means and KNN

• Accuracy – Data correctly represents real-world values
• Completeness – No missing or incomplete records
• Consistency – Uniform data across systems
• Validity – Data follows defined formats and rules
• Timeliness – Data is up-to-date and available when needed
• Uniqueness – No duplicate records

• Data minimization
• Consent management
• Transparency in data usage
• Right to access and deletion
• Purpose limitation

Scalability and Performance are critical aspects of data engineering that ensure systems can handle increasing volumes of data and user demands while maintaining speed, reliability, and efficiency. As organizations grow, their data infrastructure must scale seamlessly without performance degradation.

• Vertical Scaling (Scaling Up)
• Horizontal Scaling (Scaling Out)

• Volume – Massive amounts of data
• Velocity – High-speed data generation
• Variety – Multiple data formats (structured, semi-structured, unstructured)

• Databases (SQL & NoSQL)
• Cloud applications (CRM, ERP systems)
• IoT devices
• Third-party platforms
• Web services