Atharv Gyan

Data Science, Data Analysis, and Data Engineering: What's the Difference? Data science, data analysis, and data engineering are all closely related fields that involve working with data. However, there are some key differences between these three disciplines. Data Science Data science is a broad field that encompasses the collection, analysis, interpretation, and presentation of data. Data scientists use a variety of tools and techniques to extract insights from data, including machine learning, statistical analysis, and visualization. Data scientists typically have a strong background in mathematics, statistics, and computer science. Data Analysis Data analysis is a more focused field than data science. Data analysts use data to answer specific questions or solve particular problems. They typically use a variety of tools and techniques, such as SQL, Excel, and Tableau. Data analysts typically have a strong background in mathematics, statistics, and business. Data Engineering Data ...

In the realm of database management, there exists a category of data that is inherently dynamic and time sensitive. This type of data evolves over time, capturing changes, updates, and historical snapshots. Managing such time-varying data presents unique challenges and requires specialized techniques to ensure accurate storage, retrieval, and analysis. This is where the concept of temporal databases comes into play. Temporal databases are designed to handle time-varying data within the framework of Relational Database Management Systems (RDBMS). They enable the storage and manipulation of data while maintaining temporal aspects such as valid time (the time period during which data is considered valid) and transaction time (the time at which data is recorded or modified). There are several key components involved in managing time-varying data within an RDBMS: Temporal Data Model : Temporal databases typically extend the traditional relational data model to incorporate temporal dimension...