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Time Series VS Relational Databases

September 15, 2021

Time Series VS Relational Databases

When it comes to server administration, databases are an essential component of the backend infrastructure. Choosing the right type of database is crucial for the system's performance, scalability, and reliability. In this blog post, we will compare two popular types of databases: time series and relational databases.

Time Series Databases

Time series databases are designed to handle large volumes of data that are timestamped or time-tagged. They are optimized for fast ingestion of data and real-time analytics. Time series databases store data in a de-normalized format, which means the data is optimized for reads rather than writes.

Time series databases are widely used in the IoT (Internet of Things) industry, where sensors gather large volumes of data that need to be stored, processed, and analyzed in real-time. Time series databases can handle massive amounts of data and are capable of scaling horizontally to handle increased volumes of data.

Relational Databases

Relational databases are a traditional approach to data storage and management. They are designed to manage structured data, typically using SQL (Structured Query Language) to interact with the data. Relational databases store data in a normalized format, which means that each data point is stored separately, reducing redundancy.

Relational databases are widely used in enterprise applications, where data is organized into tables with defined relationships between them. Relational databases are highly reliable and widely used in production environments.

Comparison

Now that we have an understanding of the two types of databases let's compare them.

Performance

When it comes to performance, time series databases are optimized for real-time analytics and large data ingestion. Time series databases can handle large volumes of data and have a low write latency. In contrast, relational databases have a high write latency, making them unsuitable for real-time analytics.

Scalability

Time series databases are highly scalable, capable of handling increased volumes of data without experiencing significant performance degradation. Relational databases are also scalable, but their scalability depends on their architecture and design.

Data Structure

Time series databases store data in a de-normalized format, which makes them optimized for reads. Relational databases store data in a normalized format, reducing data redundancy.

Use Cases

Time series databases are used in IoT applications, real-time analytics, and DevOps monitoring tools. Relational databases are used in enterprise applications, financial systems, and e-commerce platforms.

Conclusion

The choice of database depends on the specific application and its requirements. Time series databases are suited for real-time analytics and IoT applications, while relational databases are suitable for traditional enterprise applications. Both types of databases have their strengths and weaknesses, and it is crucial to weigh them before making a decision.

Overall, it is recommended to assess your needs and consult with experts before making a decision.

References

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