Data storage and analysis are the two utmost challenges for businesses, whether small or large. The amount of Big Data produced has immensely escalated. Recording this information in a cost-efficient manner and securely is one of the top priorities of businesses, and this is where the cloud is a perfect suit. Thus, hiring skilled data analysts, data engineers, and data scientists has begun to trend.
Besides being an expert on various tasks like data analytics, statistics, and programming, a data scientist is also expected to operate on the latest platforms in which the businesses store data.
Let us first explore the proper definition of--- Data Science & Cloud Computing.
In simple words, data science is a study of data. It consists of methods such as recording, storing, and analyzing information to pull out useful data effectively. The main aim of data science is to get an in-depth understanding and knowledge from any sort of data, whether structured or non-structured. Data science is closely linked to arithmetic studies, including gathering, arranging, analyzing, and presenting information.
The foundation of information analytics in cloud computing is cloud computing itself. Cloud computing is worked around a series of--- Hardware & Software that can be distantly accessed via any internet browser. Typically records and programs are shared and operated by various users, and the information is distantly incorporated instead of being indexed on the client’s hard drives. In cloud computing, like tracking social media engagement and statistics, analytics is implementing the standards of analytics of data housed on cloud drive instead on individual servers or disks.
Signification of Data Science with Cloud Computing
Cloud computing and data science walk together, hand in hand. A data scientist usually examines various types of information that are stored in the cloud. The rise of Big data has compelled businesses to save a vast amount of information online, and there is a requirement for data scientists. To get a deep knowledge of data science, you can take on particular courses to get you there.
Below are the sorts of data a data scientist is possibly to work in the cloud;
Examine structured, semi-structured & unstructured information.
Analyze multiple sets of information, regardless of the size, format, etc.
Study them to draw insights
But the problem with such information is that it often sits in different storehouses. Keeping in mind that the storage is now economical, and the open-source platforms and tools are accessible for data experts, cloud is the key.
Use of Cloud Computing
Data scientists can use cloud computing platforms like Windows Azure to enable access to programming language, tools, and systems, both for a fee and for free.
Data scientists usually find it convenient to use MapReduce tools such as Hadoop to store information and retrieve tools like Hive and Pig. They use other languages as well Python and Java, to script programs.
It is usually observed that data scientists utilize two kinds of tools: the open-source ones, like R, Python, Hadoop system, and other scalable machine learning tools and many more commercially accessible ones like Tableau, Oracle RDB, MS SQL, and BusinessObjects.
Considering the size of the information and the availability of tools and platforms, knowing the cloud is relevant and the key for a data scientist.
Internet of Things (IoT)
As per Gartner, IoT is expected to have 26 billion devices by the end of 2020. Now you can think about the data produced by this interconnection, and almost all of it will be available on the cloud. Hence, there is a requirement for flexibility, various processing frameworks, and different data sets, and data science is well established with cloud computing.
A lot of advantages from data analysis comes from its ability to identify patterns in a set and make forecasts about past experiences. The process is typically called data mining, that simply means finding patterns of information sets to understand the trends better. Despite all the advantages, the big data and data analysis offers, a lot of their potential is missed since employees lack fast and credible access to said information.
According to Gartner, 85% of Fortune 500 companies cannot avail of the complete benefits of their big data analytics due to lack of availability of information, engendering them to miss possible opportunities to better interface with and match the customer requirements.
As the study shifts towards cloud drives, data analysis gets accessibility as company staff can avail company data distantly from anywhere, relieving them from being trapped to local networks, and thus making more information available.
Time Warner recently disclosed its data analytics cloud framework, enabling 4000 employees to efficiently use sales data with an expectation to get increased revenues.
Apart from its increased availability and usability, big data analysis on cloud drives also exports many IT requests, like hosting & regulating servers, to cloud service providers.
Businesses can spend less funds on servers and instead concentrate on strengthening their employees and product. Hence, cloud drives help smaller firms get into the big data game, enabling smaller companies to improvise against larger companies within their sector.
Cloud makes the technology immersive, for good or bad. There seems to be no thin line nowadays between humans and an ever-increasing computing environment. Innumerable transactions are enabled by the cloud today. Medical systems, the financial world, media companies, educational facilities are all empowered with the support of the cloud computing platforms.
Today, companies invest much of their resources on two aspects to remain lucrative, including big data and ensuring that data remains in the cloud. Processing information and moving it to cloud organizations accrue two pros handling large sets of information for decision making and mitigating the overall expense of infrastructure.
This article threw light on significant aspects of data science and cloud computing. Cloud-based computing & architectural ideas are essential to understand when working on data solutions or needing extra computing resources and power. The opportunities the cloud-based solutions offer to our experts are simply outstanding, and thus making it simpler for data scientists to do their job.