The Data Blog
Cybersecurity is one of the largest growing industries for all types of employment, including scam artists and hackers looking to make a few easy bucks. Due to the pandemic and its financial repercussions, it's more important than ever to make sure you keep yourself safe and avoid anything suspicious online.
CNBC warns that scammers are looking to target younger audiences with empty promises to forgive student loans and file taxes so that malicious software and patiently waiting hackers may steal PII (personally identifiable information), important documents, financial assets, credit card information, and more from right under their noses. This is of immediate concern especially during tax season and because stimulus checks are rolling out from the IRS, so it's important to keep your internet connection private and anti-malware software up to date.
This is not the first nor the last time scammers have tried taking advantage of a bad situation to make a quick profit which is why it's even more crucial we find new ways to combat malicious attacks coming from the cyber world.
As you may expect, the relationship between big data and the cloud is quite complex, but very efficient for all parties involved. Normally, big data can be limited by storage space, processing time, and cost. However, cloud computing can compensate for all of this; with a much larger amount of storage, faster processing, and cheaper cost, cloud computing is big data's best friend.
No longer do analysts and programmers need to run simulations and execute thousands of lines of code just to wait hours on hours to see a bug or two crashed their program meaning they'd have to restart the entire operation. By utilizing the cloud, big data is able to be run and processed in a fraction of the amount of time it used to take.
While it's possible to have one without the other, industry trends are pointing towards the relationship between cloud computing and big data being the next big boom; now that we have systems and services capable of analyzing all of this data, we can continue to improve the process.
So what does this mean for synthetic data? Larger and larger sets of synthetic data can also be used in combination with cloud computing for very similar results; machine learning to train and test models can be run with larger synthetic datasets allowing the job to be done with both more precision and speed. Artificial intelligence solutions can be vastly improved just by taking into account the relationship of cloud computing and large amounts of synthetic data, and we loo forward to the day that they are.