The statistical method is where all the data from all the machines are sent to a central system. Then the central system tries to determine the most probable combination of variables to determine which dataset was lost. This is because, in this method, a configuration change may lead to losing a dataset or missing a configuration change.
Wouldn’t this process reduce the storage space of the system?
Yes, but it could also allow for data loss. If we are storing the exact replication of all data, data loss would occur even when there are cases of large configuration changes.
If we are storing the data through different methods, or different databases, we would know which data has been lost by comparing them.
Which method is used to determine data loss?
We could use one database to store all the data and then see which parameter has been affected. We could then see the impact of a change and proceed accordingly.
Wouldn’t this process require lots of memory to store all the data?
This is true, but there is also another way to store all the data in memory. Instead of storing data in a database, you could also store data in the memory of hardware storage.
If we have a full-disk machine, we could store all the data in memory. Even if we were to take one random read from the disk and find a bug, the data is already stored in memory. Also, we are comparing it to all the other data which we already have in memory. The probability of this bug was already calculated.
The method of storing the data in memory or accessing it through an internal machine is not free from problems. Such operations, in general, are less efficient. However, in this case, the costs can be offset through a combination of using a database that does data storage and analysis in parallel and using multiple machines, each with a dedicated storage system. This would lead to less CPU usage.
Is using parallelized data analysis important?
Although parallelized data analysis may not be as efficient as data stored in memory, it would prevent data loss. Also, we would have more space to store the data because we would not be storing multiple datasets on the same machine.
Is using parallelized data analysis necessary?
We could save some of the costs of parallelized data analysis, but would not be saving all of them. Since using a database with real-time analysis would be very expensive, we should have enough money to buy an extremely powerful server, with a good amount of memory.
In case we use a database that is capable of parallelized analysis, we should perform data analysis in one machine. After all, we cannot afford a server with several disks if the database has already consumed most of the physical space.
When should you use a database with real-time data analysis?
There are cases in which storing data in memory or accessing it through a database with real-time data analysis can be very efficient. For example, we can store data in a database in RAM and then access it in parallel in a multi-machine network.
Then, we could compare different configurations and when something has changed. We could take a snapshot of the database and start a new analysis. This would provide a very fast result, without consuming much storage space.
Wouldn’t you use a database with real-time analysis only for situations where data loss could cause enormous losses?
Yes, and in such cases, we should be doing real-time analysis. In fact, in some cases, we would want to store the data in RAM in the database. Then if a configuration change happens, we could access it to see what is changed, and go back and adjust it accordingly.
If there were no issues in terms of saving data, would you rely on using a database that is not necessarily much efficient?
It depends on how important saving data is for you. This depends on several factors, including how much of your data is changing. This method is known as differential data analysis, where only a subset of the data is saved and compared with the whole data. This would also save extra money and storage space.
Is it possible to use a database that is not necessarily efficient to store data?
It depends on how frequently you are accessing data. If the amount of data is usually changing. Then you can save a lot of space using a database with lesser storage. If you do not usually access the database. Then it is unlikely that you will need to use a database with less storage. However, even if you do access data frequently, the cost of storing data in a database may be a much greater burden.
Where should you look for a database with low costs for storing data?
The cheapest database for storing data in a small amount of space is Google’s Bigtable. It uses small key-value tables which make it relatively cheap to store the data. The cheapest way of accessing this database is using an internal network that has the storage directly connected to it.
The search efficiency is very good in terms of searches for the data in the database. Even after filtering, the costs are low for performing searches for different queries. So it would be a good option if you are able to rent a small rack with storage in it and you can access the data from anywhere in the world using any internet connection. Also, it is an important database that you can use for storing data in small sizes.
We could also find other databases with similar costs, which might be suitable for storing data in very small sizes. For example, MongoDB is very fast when you compare it to other database systems because it uses JSON data structures instead of strings. But there is also a database with similar costs that is called Pig, which also uses JSON data structures. It is also an efficient database for storing data in small sizes.
