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 any time 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. 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 healthcare 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.
‘You cannot separate cloud computing from data science’
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.
The expanding role of the cloud
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. It, rather than the scientist, determines the speed and quality of the work.
With the cloud, data scientists are able to research larger sets of data without the limitations of their local workstation. Additionally, relying on the cloud for data storage can lead to a reduced cost of infrastructure, as using the cloud no longer requires a physical server. This enables smaller companies to compete with larger players in emerging markets.
By employing the cloud, data scientists can pay as they go for their storage and usage rates. Having the ability to scale up or down as needed can shrink wait times for tasks such as testing hypotheses and training algorithms, allowing data experts to complete tests faster and get jobs done more quickly. No longer are the data scientists limited by their local machines.
A wider array of tools and improved analysis
In addition to data storage, many cloud companies provide a variety of software and tools to help data scientists with artificial intelligence, analytics and data visualisation. For instance, Azure offers pre-configured virtual machines for modelling, development and deployment. It also offers valuable programming languages such as Azure Machine Learning, Azure Cognitive Services and more.
Furthermore, data experts are often granted immediate access to open-source frameworks, which is software that can be leveraged for machine learning, data manipulation and analysis. These open-source frameworks are already installed via the cloud, saving data scientists time from having to install them manually.
Data – whether it’s structured, semi-structured or unstructured – often sits in disparate silos, locked in one application or database, making it inaccessible for other uses.
To develop meaningful insights, isolated data streams must be combined so that the data scientist can analyse them effectively. The cloud helps unlock these data silos, in turn helping data scientists unlock the valuable insights within that data.
Third-party cloud migration tools can help companies move data to the platform where it’s needed. These tools are even effective with multiple cloud workloads. When identifying the ideal tool for migrating data, look for those that are SaaS-based and employ automation. This enables a swift and secure transfer of data at scale.
At a time when companies view data as one of their most important assets, the need for data scientists has never been greater. They will shape the future of data-driven organisations, but to do so they’ll need a strong understanding of data-mining techniques, programming languages and cloud computing.
Only then will they be able to reap the full potential of the cloud’s state-of the art advanced analytics and tools to quantify, interpret and glean insights from that data. By leveraging cloud computing with data science, data experts can unlock new possibilities for what they are able to achieve.
Mark Rochester is the principal product architect at BitTitan, an IT migration company that enables services providers to adopt the cloud.