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Cloudspend part 3: Optimize data modeling

Gerard Zuidweg 22-8-2023 8:57
Categorie├źn: Blog, Review

In this third and final blog in the series, Gerard Zuidweg, co-owner of OptimaData, discusses the importance of optimizing data modeling to effectively manage and reduce cloud costs. As companies migrate to the cloud, closely monitoring and controlling cloud spend is essential. Gerard shares seven practical techniques for taking a smart look at your data model. That way, you can optimize your database usage and reduce your cloud costs.

1. Standardization: Reduce redundancy and optimize data consistency

Normalization is a process of organizing data in a database to reduce redundancy and improve data consistency. Normalized data requires less storage space, reducing the cost of storage. By normalizing your data, you can reduce the number of tables needed to store that data, reducing the resources needed to manage and store it.

2. Denormalization: Improve query performance by using redundant data.

While normalization is essential to reduce data redundancy, it can also affect database performance. Denormalization is a process of creating redundant data to improve query performance. By denormalizing your data, you can improve query performance and reduce the number of resources needed to process queries.

3. Data types: Choose smart data types to save storage space.

Choosing the right data types for your data can have a significant impact on the cost of storage. Using data types that require less storage space can lower the cost of storage. For example, if you use the data type INT instead of the data type BIGINT, less storage space may be needed to store data.

4. Data compression: Reduce data size and reduce storage space costs.

Data compression is another technique that reduces the size of your data by removing redundant or repetitive data. Compression can lower storage costs by reducing the amount of storage space needed to store data. Most cloud providers offer data compression as a built-in feature.

5. Partitioning: split data into manageable parts and reduce resource usage.

Partitioning is a nice concept that allows you to break down your data into smaller, more manageable pieces. By partitioning your data, you can reduce the number of resources needed to process queries. Most cloud providers offer partitioning as a built-in feature, so take advantage of this to reduce cloud costs.

6. Indexing: Increase query performance with efficient data structures.

Indexing also improves query performance by creating a data structure that allows you to quickly retrieve data based on specific criteria. By using indexing, you can reduce the number of resources needed to process queries and improve query performance. However, be careful not to create too many indexes, as this in turn can increase storage costs.

7. Caching: Store frequently used data in memory for faster response and reduce resource requirements.

Finally, caching allows you to store frequently used data in memory, reducing the number of requests to the database and reducing the number of resources needed to process queries. Most cloud providers offer caching, so it can't be made much easier.

In brief

In conclusion, reducing and managing cloud spends by optimizing data modeling requires a combination of techniques, including normalization, denormalization, using appropriate data types, data compression, partitioning, indexing and caching. By implementing these techniques, you can optimize your data model, reduce cloud costs and also improve the performance and reliability of your applications and services.

Want to know more?

After reading these three blogs, are you curious how we can help you manage cloud costs and how exactly that works? Feel free to contact us! We'd love to think along with you.

Cloudspend part 1: The move to the cloud

Cloudspend part 2: Optimizing database usage

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