Time Series Data Modeling
Time series data modeling is concerned with identifying, organizing, and studying only data collected in the form of time series such as sensor readings, financial metrics, or user activity logs. Major factors in time series data model design are especially:
- Timestamps: Provisioning timestamps or time intervals for more precise information about time data was gathered, doing so would allow the analysis of trends and the detection of temporal fluctuations.
- Aggregation and Compression: Aggregation and compression of time series data to limit the storage space and improve querying and analysis efficiency.
- Data Retention Policies: Data retention, archiving and deletion policies should be developed based on the prevailing business needs as well as regulatory compliance.
Data Modeling in System Design
Data modeling is the process of creating a conceptual representation of data and its relationships within a system, enabling stakeholders to understand, communicate, and implement data-related requirements effectively.
Important Topics for Data Modeling in System Design
- What is Data Modeling?
- Importance of Data Modeling in System Design
- Types of Data Models
- What are Entities, Attributes, and Relationships?
- Data Modeling Notations
- Normalization Techniques
- Denormalization Strategies
- Data Modeling in NoSQL Databases
- Time Series Data Modeling
- Real-world Examples of Data Modeling
- Best Practices for Data Modeling
- Benefits of Data Modeling
- Challenges of Data Modeling
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