Featured Post

8 Ways to Optimize AWS Glue Jobs in a Nutshell

  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif

Data mining Real life Examples

Data mining is a process to understand about unused data and to get insights from the data. You need a quick tutorial and examples to perfect with this process. The best example is the Backup data business use case to mine the data for useful information.

The backup data is simply wasted unless a restore is required. It should be leveraged for other, more important things. This method is called Data Mining Technique.

For example, can you tell me how many instances of any single file is being stored across your organization? Probably not. 

But if it’s being backed up to a single-instance repository, the repository stores a single copy of that file object, and the index in the repository has the links and metadata about where the file came from and how many redundant copies exist.
Data mining Real life Examples
By simply providing a search function into the repository, you would instantly be able to find out how many duplicate copies exist for every file you are backing up, and where they are coming from.

Knowing this information would give you a good idea of where to go to delete stale or useless data. 

The complete knowledge of Data mining is a plus point to start further on this. 

After all, the best way to solve the data sprawl issue in the first place is to delete any data that is either duplicate or not otherwise needed or valuable.

 Knowing what data is a good candidate to delete has always been the problem.

Data Mining vs Data Science

There may be an opportunity to leverage those backups for some useful information. When you combine disk-based backup with data deduplication, the result is a single instance of all the valuable data in the organization. This is the best data for data mining.
With the right tools, the backup management team could analyze all kinds of useful information for the benefit of the organization and the business value would be compelling since the data is already there, and the storage has already been purchased. 

The recent move away from tape backup to disk-based deduplication solutions for backup makes all this possible.

data mining vs data science

Being able to visualize the data from the backups would provide some unique insights. As an example, using the free WinDirStat tool.

A best use case is, I noticed I am backing up multiple copies of my archived Outlook file, which in my case is more than 14GB in size. If you have an organization of hundreds or thousands of people similar to me, that adds up fast.

Top Questions ask Yourself if the Data Mining tool is needed
  • Are you absolutely sure you are not storing and backing up anyone’s MP3 files?
  • How about system backups?
  • Do any of your backups contain unneeded swap files?
  • How about stale log dumps from the database administrator (DBA) community? 
  • What about any useless TempDB data from the Oracle guys? 
  • Are you spending money on other solutions to find this information? 
  • Are you purchasing expensive tools for email compliance or audits?

Advantages of Data mining
  1. The backup data could become a useful source for data mining, compliance and data archiving or data backup,
  2. Also, bring efficiency into data storage and data movement across the entire organization.


Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

How to Check Kafka Available Brokers

SQL Query: 3 Methods for Calculating Cumulative SUM