In modern context of computing technologies, we are dealing with four types.
These are:
- Cloud Computing
- Edge
- Fog Computing
- Quantum Computing
Depending on the path of enterprise IT modeling and automation in place, you can choose any for the data management and analytics. Various Big Data Online courses cover the various computing technologies and analytics. Out of these four, only Edge and Cloud computing would make the biggest impact on the latency and resourcefulness of Data, and Analytics.
Here are different Big Data scenarios for these computing specializations.
Cloud Computing for Big Data
The rise of Big Data Cloud is proving to be facilitator for all other forms of Computing and Analytics.
The Cloud is a really busy word for most analysts today. The crazy noise with Cloud Computing for Big Data Analytics is only growing louder and louder. In a big data ecosystem, your Cloud Computing platform should be able to sync billions of data points from the internet in real time.
Putting data into Cloud can open up new opportunities for new-age companies who deal with billions of data points daily.
For companies that are constantly improving their products and services, relying on traditional can prove too much to ask from a single infrastructure. That’s why, we have growing affinity in the industry for Platform as a Service and Infrastructure as a Service (PaaS and IaaS) providers. Microsoft, Amazon, Google and Rackspace provide the backbone of Cloud Computing by renting out their data storage space.
According to Accenture report, companies that don’t embrace Cloud Computing for Big Data management face a definite extinction in the next few years.
Edge Computing for Refined Data
Edge computing is putting most Cloud providers on a steroid mode. It puts a combination of IoT devices, data storage, connectivity and analytics on a super efficient infrastructure. For companies that are not delving into Big Data, Edge is the way out. If you have big data and bigger questions to solve, stay away from Edge. IN a recent survey, Cisco showed that IoT devices would throw out 850 Zetabyts of data per year by 2021. That’s simply staggering by all Big Data standards. And yet, we wouldn’t list this amount of data in our Big Data repository.
It would be only 10% of the total data produced per year from all of internet. In the coming years, we will see a combination of Cloudnet and Edge forming the center of all powerful resources running on laptops, smart phones, connected cars, and IoT devices.
If you are pursuing Big Data Analytics as a specialization, lookout for opportunities in DevOps Applications, AI Delivery models, and ML Modeling systems. AI ML for IT operations is all jacked up with cutting edge force coming into play with players like Google Cloud, SAP HANA, IBM Red Hat, AWS, Cisco, Dell EMC, and HPE making bigger wave in the industry. In an interconnected Cloud market, mastering Big Data Computing and Analytics should be your mission “numero uno”.
