Cloud-native data systems are becoming increasingly popular due to their scalability and flexibility. Some people also prefer them because they are easier to manage than traditional data systems. However, there are several factors that need to be considered while designing a cloud-native data system.
Resiliency
Usually, cloud-native data systems are designed to be more resilient than traditional data systems. This is because they can take advantage of the scalability and flexibility of the cloud. However, it is important to ensure that the data system is able to recover from any potential failures.
One way to increase resiliency is to replicate data across multiple regions. This way, if one region experiences an outage, the data will still be available in other regions. Another way to increase resiliency is to use a distributed storage system, such as Apache Hadoop. Hadoop is designed to store data across multiple nodes, which makes it less likely that all the data will be lost in the event of a failure. Lastly, it is important to have a good backup and recovery strategy in place. This way, if the data system does experience a failure, the data can be restored from the backups.
Performance
The performance of the data system is also an important factor to consider. This is because the data system will need to be able to handle the workloads of the applications that it is powering.
There are several ways to improve performance. For example, caching can be used to improve response times by storing frequently accessed data in memory. This means that the data can be accessed more quickly than if it was stored on a disk. Another way to improve performance is to use a distributed storage system, as mentioned above. Hadoop is designed to spread the data across multiple nodes, which can help to improve performance by distributing the workload.
Thirdly, it is important to choose the right instance type when deploying the data system on the cloud. This is because different instance types have different amounts of CPU and memory, which can impact performance. Lastly, it is important to monitor the performance of the data system and tune it accordingly. This can be done using tools such as New Relic or DataDog.
Security
Security is also an important factor to consider while designing a cloud-native data system. This is because the data system will be storing sensitive information, such as customer data.
One of the best ways to improve security is to encrypt the data at rest. This means that even if the data system is compromised, the data will be unreadable without the encryption key. Another way to improve security is to use a private cloud instead of a public cloud. This is because a private cloud gives you more control over who has access to the data system. Lastly, it is important to have a good security policy in place. This should include things such as two-factor authentication and least privilege.
Cost
When designing a cloud native open data platform, it is important to consider the cost. This is because the data system will need to be deployed on the cloud, which can be expensive. Consider using a cost-effective storage system, such as Experion. These storage systems are designed for the cloud and can help to reduce costs.
It is also important to consider the cost of the instance type when deploying the data system on the cloud. As mentioned before, different instance types have different prices. Choose an instance type that is able to handle the workloads of the data system but is not too expensive. Lastly, it is important to monitor the usage of the data system and scale it accordingly. This can help to reduce costs by ensuring that you are only paying for the resources that you need.
Scalability
Scalability is also an important factor to consider because the data system will need to be able to scale as the number of users increases. There are several ways to improve scalability. For example, you can use a horizontally scalable database, such as MongoDB. This type of database can be scaled by adding more servers to the cluster. Lastly, it is important to monitor the usage of the data system and scale it accordingly. This can help to ensure that the data system is able to handle the increased load.
Ultimately, the choice of whether to use a cloud-native data system or not depends on the needs of the application. If the application is critical, then it is worth considering using a cloud-native data system. This is because they can offer better availability and performance. However, if the application is not critical, then it might not be worth the extra cost.
Author: Sylvia James