Driven data enthusiast aspiring to become a professional data scientist, with a keen interest in mastering data engineering and AWS technologies. Passionate about extracting insights from complex data using machine learning and data analysis techniques.
Passionate data enthusiast aspiring to master data engineering, AWS, and ML. Strong problem-solving skills. Quick learner, eager to contribute to innovative teams.
OLAP VS OLTP OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) serve distinct purposes: Purpose: OLTP: Manages daily transactions like insertions, updates, and deletions in real-time, focusing on data integrity. OLAP: Supports complex queries and aggregations for analytical tasks, prioritizing read-intensive operations. Database Structure: OLTP: Normalized structure with minimal redundancy, ensuring transactional integrity. OLAP: Denormalized or star/snowflake schema for efficient multidimensional analysis. Query Patterns: OLTP: Handles simple, short transactions for real-time processing and concurrent access. OLAP: Manages complex analytical queries involving aggregations, joins, and calculations for insights. Performance Requirements: OLTP: High concurrency, low response times for individual transactions. OLAP: Fast query responses, even for large datasets and complex queries. Data Volume: OLTP: Deals with smaller data volumes, frequent write operations, and current data. OLAP: Handles larger data volumes, including historical data, with aggregated and summarized data.
Showing 1 to 1 of 1 results
