How cloud computing can unlock new data science possibilities
BitTitan’s Mark Rochester examines the benefits of the cloud in data science when big data keeps getting bigger.
Big data is on the rise and there is no sign of it slowing down anytime soon. IDC forecasts that by 2025 the global datasphere will reach 175 zettabytes. For context, a zettabyte translates to roughly 1,000 exabytes, or 1bn terabytes, or 1trn gigabytes.
Going a step further, a petabyte – or 1m gigabytes – equates to the volume of more than 3.4 years of 24/7 full HD video recordings. Multiply that capacity by 1m and that gives an idea of where our global data usage is heading.
As data grows, so does the complexity of managing it. This is why tools such as machine learning and cloud computing are becoming essential for data scientists.
Most companies know that capturing and using data is vital for their ongoing success. In almost every industry – from automotive to education and health care to manufacturing – data serves as the backbone of future innovations. Businesses will continue to rely on this data to glean insights into their industries and run their operations more efficiently.
Given the coming increase of such data, the question becomes how to manage it. To process and analyse all the information available to organisations, they will need a huge number of data analysts and data scientists adept at using machine learning and cloud computing.
An understanding of cloud computing is essential since so much of this data is being stored there. Without the cloud, the usefulness of data science is greatly diminished.
You cannot separate cloud computing from data science. As the amount of data continues to grow, securely storing data in a practical, cost-effective manner has become a priority and the cloud is equipped to handle such colossal data loads.
Cloud storage provides businesses with flexibility and agility, better scalability, and more robust security. All of this comes at a lower total cost of ownership. Organisations have taken note and are relying on the cloud for storing large sets of data.
However, this means that in addition to an expertise in data mining, statistics and probability, data analysts and data scientists also need to be skilled in computer science and cloud computing. They need to know how to leverage powerful platforms such as Amazon Web Services S3, Microsoft Azure, Google Cloud Storage, Oracle Cloud, IBM Cloud or Alibaba Cloud.
Leveraging the cloud and adopting cloud computing skills bring many benefits. Consider some common challenges that data scientists encounter.
Generally, a data scientist completes most of their processes on a local computer. However, the limited power of their CPU is unable to execute these tasks in a timely manner, if they are able to be carried out at all. In addition, large datasets are often too big to be stored in the system’s memory. In essence, a data scientist is limited by their local computer.
