The world of tech is vast and within that wide range, there are a myriad of job positions that intersect and collaborate with one another, forming a network of assistance and support. Two positions that do make contact sometimes are cloud architects and data analysts.
You may think: how do these two professionals end up collaborating with one another when one is concerned with the cloud and the other is focused on analyzing data? Good question! But first things first, what are they and what do they entail for the people who do work with the cloud and data?
Cloud computing revolutionizes data analysis by providing scalable resources and powerful computational capabilities. For instance, platforms like AWS, Google Cloud, and Microsoft Azure enable data analysts to process large datasets efficiently without the need for costly on-premises infrastructure. These platforms support various data analysis tools and services, such as BigQuery for large-scale data processing and TensorFlow for machine learning.
According to a report by Gartner, 85% of enterprises will have a cloud-first principle by 2025, owing to the efficiency and scalability of cloud computing. Moreover, a study by Flexera indicates that 92% of organizations are implementing multi-cloud strategies to enhance their data analysis capabilities.
What is Cloud Computing?
We have all heard of the cloud, but what else do we know about it, other than that it stores data? The following cloud computing definition from Merriam Webster dictionary may help clear up your doubts:
The practice of storing regularly used computer data on multiple servers that can be accessed through the Internet.
The keywords here are storing, multiple servers, and accessed through the internet. The cloud is used for storage, management, and processing of data that does not depend on a local server or personal computer. Instead, a slew of cloud service providers such as Amazon Web Services, Microsoft Azure, Google Cloud, or iCloud provide users infrastructure as a service (IaaS), platform as a storage (PaaS), and software as a service (SaaS), which provide the following functions:
IaaS: because users are given access to virtual machines, networks and storage, they can create their own computing environment.
PaaS: this service is best for developers and can be used to test, build, modify and open applications with no fear of affecting the previously established infrastructure.
SaaS: users can access software instantly over the internet, removing the need to install or update anything.
To access any cloud storage, the internet is a must since it’s used as the connection between your device and the network of remote servers that house all of your data. Cloud computing services require payment to store data on their private servers because they are providing an essential service to their customers.
The benefits of cloud computing cannot be understated, considering the cloud gives users the ability to access storage from anywhere as long as they have access to the internet. Not only does it increase accessibility for completing tasks as high up as on a plane crossing the Atlantic, but it also provides a reliable and strong infrastructure for your data storage needs.
What is Data Analytics?
The importance and influence of data analytics is not only in the tech world, but also in the political, financial, and business realms: where there’s data, there’s knowledge, and knowledge is power. Although data analysis sounds like it would be simple, it is a complex process that includes many steps to achieve their final results:
Main objective: defining the main objective of the data collection and analysis paves the way for the rest of the process.
Data collection: you must collect the data that you want to analyze, which can sometimes be a large pool of information.
Cleaning and preparation: the data must be sifted through, evaluated for viability, and prepared for the analysis, since there may be inconsistencies and outliers that impact the data.
Exploratory data analysis: this first encounter with the data will give the data analyst first impressions of the content, patterns, outliers and other potential results. Data analysts create visual representations and follow leads on patterns that may appear when exploring this data.
Data modeling and analysis: this would be the meat and potatoes of data analytics; data analysts employ their data analysis tools and strategies to dig for any insights and answer any questions that they had upon setting their initial main objective. These techniques may include regression modeling, factor analysis, time series analysis, clustering, and classification.
Interpretation and insight generation: the collected data has gone through various analytical techniques and now insights can be determined and questions answered. Analysts also identify any trends, patterns, relationships, and other important information that could provide them with more useful details.
Data visualization and reporting: by using all the information from the data, data analysts create visual representations that show everything in an easily understood manner as well as reports that detail the complete data analytics results.
Going through this process, data analysts will be able to provide their insights and other important information to help their company make any necessary adjustments or improvements.
Although the higher-ups make the final decisions upon seeing the data, the data analytics team has a lot of influence and power to make big changes to their products and the future of their company. Due to the importance that companies place on the trends and insights that they provide, data analysis jobs are in high demand.
Photo by Myriam Jessier on Unsplash
What Responsibilities do Cloud Architects Have?
Although they have the word architect in their name, cloud architects don’t have anything to do with designing houses; however, they do create, manage, and handle the cloud infrastructure at companies. Upon arrival at a company, they frequently establish the cloud network for their offices.
Cloud architects are also responsible for optimizing the company’s use of the cloud computing system and carrying out cloud-based solutions in the case of any issues by using the following:
Front end platform: all of what the client interacts with includes the front end cloud infrastructure and when clients will use this software to gain access to the cloud.
Back end platform: the back end cloud infrastructure is the hardware and software with which clients don’t directly interact. Servers, network devices, storage, hypervisors, and virtual machines are examples of components of back end cloud infrastructure.
Cloud based data delivery models: the three main data delivery models that are provided to companies are the three mentioned above: IaaS, SaaS, and PaaS.
Since there aren’t many degree programs dedicated to cloud computing, many cloud architects will study other degrees first then sign up for a cloud certification program where they will acquire expertise on programming, cloud infrastructure, and cloud computing security. There are many cloud computing jobs available right now since the reliance on the cloud is growing exponentially.
The Impact of Cloud Computing on Data Analysis
These two tech fields don’t have much in common in terms of their purposes, but they are both important aspects of any company. Cloud computing provides a company with a large amount of storage space, access to online applications, and a testing ground for development. Nevertheless, once the main cloud infrastructure is established, maintenance and optimization are at the forefront. Cloud architects have many responsibilities but since they are solely focused on the cloud, they do not have a high project turnover rate.
Data analytics is not a static process like cloud computing is; data analysts choose a main objective that they focus on to generate the data-driven results and they go from goal to goal, collaborating on diverse projects. Once one set of questions has been answered or insights have been achieved, they can move onto the next goal or query. This gives data analytics a dynamic feel in comparison to cloud computing: there will always be the next new question to ask or insight to figure out.
How do Cloud Architects and Data Analysts Work Together?
Can you guess what brings these two professionals together? If you guessed data, then you are correct. Both deal with huge amounts of data daily so their paths cross when there are potential data or storage issues.
Cloud architects are in charge of handling the storage of all of the company’s data and in the case of any issue, they need to troubleshoot and get to the bottom of the problem.
Source: Techopedia
Data analysts depend on having access to cloud storage since they are working with the data for a potentially extended period of time. They may need to discuss the amount of space they need, speed of the connection, or availability of space amongst other questions.
Although cloud computing and data analysis are more different than similar, data analysts do rely on them for their data storage needs, which makes this relationship an important one at the office.
In the tech world, a career in either one of them is highly desirable and as more offices go remote and ditch physical servers and computer space for cloud storage space, the need grows for both of them. At Ironhack, you can find our Data Analytics Bootcamp to become a data analyst. Check it out today!
How to Get Started with Cloud Data Analysis
Choose the Right Platform: Evaluate AWS, Google Cloud, and Microsoft Azure based on your needs.
Learn Key Tools: Familiarize yourself with tools like BigQuery, AWS Redshift, and TensorFlow.
Implement Security Best Practices: Use encryption, access controls, and regular audits.
Leverage Automation: Use serverless computing and automated workflows to streamline data processing.
Monitor and Optimize: Regularly monitor performance and optimize resource usage to reduce costs.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
