We live in a digital world where large amounts of data are getting processed every single second. Computers and the internet are essential parts of our everyday lives. As of July this year, there were more than 4.5 billion active internet users in the world. More than half of humans have a smartphone. It isn’t difficult to realize how much data gets transferred, accessed, analyzed, and processed every day.
A single smartphone user generates 40 exabytes of data every month. Now imagine how much data is being produced by 4-5 billion people with each passing moment. It is so much information that we need a sophisticated database management system to handle all of it.
Too much data
In 2016, people made around 96 million posts on Instagram. This figure has probably doubled because the number of internet users rapidly increased in the past few years. In 2018, the internet created 2.5 quintillion bytes of data.
Content creators upload at least 300 hours worth of videos on YouTube. Facebook users create 3.2 billion likes and shares every day. Twitter gets more than 5,000 daily tweets from its users. Every second, some 40,000 search queries appeared on Google. Social media alone is the source of this immense amount of data around us.
Your searches, text messages, phone calls, emails, videos, photos, music files, and documents contribute to the big data. Therefore, companies and corporations need efficient database products for managing this information.
What is big data?
Some believe that the term big data is just a hype/market expression with no definite meaning. It refers to the extensive data that can’t be handled by a standard database management system (DBMS). This concept involves 5 Vs., i.e., qualities that begin with that English letter. The efficiency of a DBMS system depends upon how many of these five qualities it’s capable of offering.
- Volume
- Velocity
- Variety
- Veracity
- Value
Some also include the term variability in this list, which means various methods to use big data. There are three categories of data, i.e., structured, semi-structured, and unstructured. Files created on Access/Excel belong to the structured group. XML files fall in the semi-structured category. The unstructured group refers to data such as results generated by a search engine.
Large data and RDBMS
There is much argument online about whether RDBMS is suitable for big data management or not. There is no chance of NoSQL unstructured DBMS rendering RDBMS obsolete. Instead, we expect that big data will coexist peacefully with relational DBMS in the future. RDBMS can correctly handle structured and semi-structured data.
Research shows that it’s capable of storing, analyzing, and processing big data as well. Many leading social media platforms are using RDBMS systems for data processing. Facebook, Twitter, Flickr, and YouTube use MYSQL for database management. It is an open-source SQL relational DBMS. It shows that relational DBMS is a viable option for working with extensive data.
TIPS FOR WORKING WITH EXTENSIVE DATA
- Be informed about the latest technological trends: The world of database management is changing by the moment. The nature and the amount of processable data are varying. So, new technologies are emerging to adapt to these transformations. The secret of successfully managing a large amount of data is to be informed about the latest trends in technology. Companies need to learn how to leverage massive data to enhance their financial performance.
- Keep creating and storing data: Richer, the data is, better is its management. You should cherish your data and avoid mercilessly throwing away useful information. If you run a business, finding more data shouldn’t be a tough job for you. Just ask your clients to whom they would recommend your brand. Generating more data isn’t just an easy task; it’s also beneficial for your business. The more significant the dataset is, the more precise the results are. Always dreaming bigger isn’t an inaccurate saying. Statistical analysis shows that a more extensive dataset helps business owners make better judgments.
- Protect your raw data from modifications: An essential tip for extensive data management is to keep your raw data in its original form. If you want to manipulate it, make a copy first. This ingenious method helps you generate a backup in case of data loss/theft. Of course, this raw data will hardly be ever useful if you’re manipulating its copy with professional care. You can store the raw data in a place where storage is cheap, but access may be expensive.
- Data needs to be synchronized: Many channels in your organization will have to access your central database. Therefore, it needs to be well-synchronized for efficient and productive intra-organization communication. Interlinked data stored on cloud devices is a recommended approach for better data synchronization.
- Demonstrate your workflow: You pick raw data, manipulate it, refine it, and then turn it into meaningful information. The entire process is essential in your research, and you should demonstrate this procedure in its entirety. It’s like a teenager solving a maths question but only writing down the problem’s final solution. If he doesn’t state the process he used to address the numerical, then the answer will be doubtful. Therefore, proper documentation of your data manipulation strategies is necessary. A simple method is to take the screenshot of your computer when you’re running a command on your DBMS.
- Metadata is also essential: Data workflow also includes the collection of metadata. What is metadata? It’s data about data; it shows how your data got collected in the first place. Try recording it in a README file for future counsel. As they say in the tech business, metadata helps analysts discover hidden meanings in big data. Metadata is rich in information and helpful for the creation of a larger dataset. Just a simple 140-character tweet has dozens of metadata associated with it.
- Be careful to keep your data secure: The data you handle and manipulate is precious. It may contain sensitive and personal information regarding your customers. You should protect this data at all costs and don’t let it fall into criminal hands. The three dangers to your data are hackers, viruses, and natural predicaments (humidity, temperature, et cetera). The most crucial key in extensive data management is to understand how to protect this data. Loss of a small amount of information is disastrous; loss of big data is a catastrophe.
Conclusion
Researchers have been expecting an imminent superiority of machines over humans since the 20th century. CPUs can attain the computing capacity of the mortal mind in less than a few decades. Artificial intelligence devices are likely to sit on the board of directors of a corporation. Computers will even put millions of people out of a job.
Machine analysis of a massive amount of data will make them more reliable than humans. Database management is becoming more dependent on high-tech computers as we’re growing to be more reliant on the internet. Companies will have to replace outdated applications and go for better DBMS products.
