Data Science in the Cloud: How Cloud Computing Impacts Analytics

Despite being around for a while, cloud computing is still a buzzword due to its versatility. It doesn’t matter whether you are interested in developing solutions, training AI models, or expanding your business operations — cloud computing can do it all. The cloud is a great way to work with data.

Data science, as you can guess, benefits greatly from cloud computing.

In this article we want to give you an idea on how cloud computing transforms data science and why you should use it.

Too Much Data: Is Cloud Data Analytics the Answer? 

Data science aims to get insights into why, how, and when certain trends occur. Needless to say, the more data you have, the more insights you can get out of it. 

However, data analysis isn’t all sunshine and rainbows. Businesses encounter two main challenges in this field: too much data and a lack of tools to analyze it.

For a better understanding of the situation, imagine a plate of food. Anyone could eat the whole plate, but we rarely see a person who could eat a whole buffet. This is the exact problem some businesses encounter with data analysis – there’s too much data to analyze.

There are a couple of options to solve this problem – let’s take a look:

• Databases: Having a centralized database is a good practice, but they could be limited in the amount of data they store. If a business doesn’t have enough computing power – especially if it works with big data – these downsides are an instant deal-breaker;
• Servers: While on-site servers can provide enough storage for data, this isn’t the cheapest option. The cost of hardware and its maintenance can become a money sink. Servers aren’t flexible to business needs, so there’s a high chance your organization will have more hardware than it actually needs.

Databases don’t solve data analysis issues on their own, and servers can be a costly solution. This leaves us with cloud computing. 

Cloud computing can provide a variety of services, such as storage, computational power, software, and analytics tools. The cloud has all the benefits of having on-site servers without high costs.

Cloud services let data scientists work with “live” data and not rely on the computational power of your device. The cloud is great not only for storage, but for data processing, too. 

Since cloud computing solves both restraints, using it for data science is a no-brainer. The benefits businesses get from this combination are so impactful that it’s hard to overlook them.

But what outcomes are possible when integrating data science and cloud computing for businesses?

Benefits of Data Science in the Cloud

The impact of cloud computing on data science cannot be overstated. It’s not just a cheaper way to store data, the cloud holds great potential. Here are the advantages of using the cloud for data analytics.

• Scalability: We live in a dynamic world, meaning businesses naturally change over time. In one quarter, you need more storage space; in the next, you need more processing power. Luckily, serverless data science solutions let businesses quickly adapt to these changes. Subscription plans are usually flexible and allow you to pay only for what you need at the moment.
• Cost-efficiency: Cost-efficiency of cloud solutions for data science is important, since data science can be pricey. While some might brush it off as “operational expenses”, in reality, the cloud will always be a cheaper option. There are no maintenance costs, no hardware investments – just pay for what you use. 

In addition, with the cloud you can create serverless data science solutions, which are much cheaper to operate compared to on-site projects.

• Accessibility: With working from home becoming a trend that’s here to stay, a lot of employees are using their own devices for work. While this has its benefits, sometimes employees’ hardware won’t be sufficient to complete tasks properly. 

Cloud computing solves this problem since all processing is happening on the cloud side. Additionally, this allows employees to work from anywhere as long as they are connected to the internet.

• Efficiency: Who will finish a project faster: one employee or a whole department? The same analogy applies to cloud computing. 

Instead of having the processing power of one computer, you have the processing power of hundreds. Needless to say, tasks performed in the cloud will be finished much faster compared to your own computer.

• Automation: When it comes to data science, one of the most important things to consider is optimal resource allocation. Usually, this process is done manually, which takes time and requires constant attention. Cloud computing can automate resource allocation, leaving data scientists to focus on other tasks.

As you can see, there are clear advantages of using the cloud for data analytics. We at CHI Software know this from personal experience since we have developed applications based on data science in the cloud. Let’s open up our portfolio and check them out.

CHI Software and Cloud-Based Data Science Solutions: Our Experience

Here we gathered a couple of examples of how cloud computing transforms data science and makes solutions more efficient. 

Cloud Data Analytics for an AI-powered Table Tennis Coach

Some might think that sports isn’t a place where technology shines. In reality, sports technologies continue to evolve, and AI is at the center of this process. Most sports solutions play the role of analytics or coaches to help athletes in training.

Our client wanted to create such a solution for table tennis. Their main points of interest were:

• AI-driven cloud data analytics;
• Creating a mobile application for more comfortable use;
• Optimized processing;
• Cross-platform functions;
• AI model adaptation for mobile.

Our client already had an AI model, but it wasn’t optimized to work on mobile devices. After assessing the main points for improvement, we began app development. The result was worth it:

• The app can analyze game videos to find highlights and edit out idle moments;
• The solution also analyzes players’ performance and provides improvement tips;
• For better analysis, the app accounts for various video angles and perspectives to provide users with more valuable insights;
• After analysis, the app generates a report on the game so that users can always access the insights.

We utilized cloud infrastructure for scalable data processing and storage to make data analysis possible in real-time. This project is still ongoing and is regularly updated with new features and improvements. 

To read more about this project, click here.

Cloud-Based Data Science Solution For Fleet Management 

There’s often a lot of external pressure around logistical operations since even the smallest mistake can lead to supply chain disruptions. Most of these errors are the result of poor truck fleet management.

Our client was looking to solve this problem for themselves. They were interested in a solution that could manage their fleet in real-time, as well as having some additional tricks up its sleeve, such as:

• Generating analytical data to facilitate better decision-making;
• Enabling customers to create their own divisions on the platform;
• Implementing data storage in addition to data analysis. 

Creating such a solution is no easy task – but we are never shy about taking on a challenge. The solution we created is full of useful functions:

• The ability to create subdivisions for customers;
• Real-time tracking of each truck on a map;
• A system of notifications and alerts to inform users when the truck is outside the predefined area of operation;
• A system to remotely monitor the state of the cargo;
• A dashboard where users can see all the relevant information;
• The ability to generate and share reports;
• Automating routine tasks, such as maintenance schedules and trailer diagnostics.

This project would not be possible without using the cloud. This combination was the most optimal option since there’s a lot of data to collect and analyze from fleet vehicles. 

The project is still ongoing. If you want to learn more about it, you can read our case study. Meanwhile, let’s take a look at another example. 

Cloud Computing and Analytics for Parcel Processing Systems

Being one of the oldest services available to humans, postal and parcel delivery companies haven’t seen much change since the Industrial Revolution. In today’s digital era, this is about to change.

With each year, more organizations invest in technological advancements for parcel logistics, and our client was no stranger to this. 

Our client was looking for a solution that:

• Could provide insights on where to improve business;
• Detects parcels in unfavorable conditions, such as poor lighting, or damaged parcels;
• Identifies labels and barcodes on parcels;
• Decreases the number of incorrectly recognized labels to reduce errors during shipment;
• Can dynamically scale.

To provide the business with insights, we used data science in sorting and route optimization. At the same time, cloud computing allowed for scale deployment. 

This project is the result of five months of hard work, which gave our client improved parcel processing. The features of our solution include:

• Accurate parcel detection;
• Precise extraction of data from parcels, such as country, zip codes, and barcodes;
• Delivery information to extend into the database;
• Automation of shipment instructions.

To read more about this project and the technologies we used, read our extensive case study. 

Cloud Data Analytics: Where to Start

Now that we have familiarized ourselves with the benefits and applications of data science in the cloud, there’s only one question left: where do you start? Luckily, our extensive cloud development expertise can help you. Let’s talk about how to leverage the cloud for advanced analytics.

Define Objectives

To get started, you need to understand what type of solution you want to develop. Conduct research, see how your competitors have integrated similar solutions, and what problems they are solving. 

Think about what business needs you want to fulfill and what features your solution should have.  

Choosing a Vendor and Their Services

There are a lot of cloud-based development vendors out there, and each offers a variety of services. But which one is the best for your cloud native data science platform? The answer depends on your goals, budget, and desired expertise.

You need to have clear goals in mind. How much computing power do you need, and how much storage do you intend to use? This will help you define which services work best for your needs. 

If you’re looking for cloud software development — consider us! We at CHI Software can offer you various cloud software services. Backed by two major cloud vendor partnerships (Microsoft Azure and AWS), we can help your business with a variety of tasks, from cloud migration to custom cloud software solutions development.

Choosing a Platform

CHI Software works with the most popular cloud platforms for data science projects:

• Microsoft Azure is popular for AI development and on-premise environment integration;
• Google Cloud Platform (GCP) is mostly used for cloud computing and analytics, as well as machine learning;
• Amazon Web Services (AWS) is a jack-of-all-trades that provides everything from plain storage to machine learning and data analysis.

Once a vendor has been chosen, it’s time to compare subscription models that cloud providers offer to find the best option for you. Check the features by using free trial offers to make a decision.

Preparing Data for the Cloud

To make any sense out of data, it needs to be cleaned first. While it’s not an easy task, clean data is what gives businesses clean insights. Data cleaning includes:

• Eliminating errors and duplicates;
• Normalizing data to one format;
• Organizing data in a logical structure;
• Providing meaningful labels to data.

These steps might sound tedious, but they are important for future usability. It’s easy to find things when each item has its own place, right? Once your team is done with these steps, your data is ready to be transferred to the cloud.

Optimizing the Cloud

While cloud computing is valuable for data science, to utilize the cloud’s full potential, you may need additional planning. In our experience, the best practices for cloud-based data science are the following:

• Cloud services come in different configurations. To make the most of cloud computing, you need to choose a configuration that suits your goals best; 

For example, one-time analysis uses low-cost and low-power cloud services, while complex analysis may cost more and offer more powerful services;

• A good call is to monitor cloud resources and adjust them for better efficiency. This will help you to avoid overpaying for computational power you don’t use. For this, you might want to use auto-scaling features if they are available;
• Most cloud services provide tools for process automation. By utilizing them, you will save time and achieve better efficiency.

While these tasks might seem challenging, with the help of the experts, they are a walk in the park. With almost two decades of experience, we at CHI Software can help you optimize the cloud to suit your business needs.

Final Words

Future of data science with cloud technologies looks brighter than ever! The cloud’s edge in computing and data science is a textbook definition of perfect synergy. However, you need experts in both fields to utilize it. 

Luckily, you have found us! We at CHI Software are proud to be experts in both, and we will gladly help you create a solution to your needs. Contact us and get ready to harness the power of data science in the cloud.

FAQs

• What are the main benefits of using cloud computing for data science and analytics?

  The advantages of using the cloud for data analytics are:
  - As long as you pay for additional storage, the cloud can infinitely scale;
  - Cloud services are cheaper to maintain compared to other solutions;
  - The cloud enables employees to work from any place with an internet connection;
  - You don't have to rely on your own hardware because cloud services provide scalable remote computational power.
  

• What types of cloud services are available for data science?

  Out of all the different cloud services available for data science, these stand out the most:
  - Using virtual machines for data processing, known as Infrastructure as a Service (IaaS);
  - Tools for data preparation, as well as other services offering a Platform as a Service (PaaS);
  - Interactive dashboards for insights visualization from Software as a Service (SaaS).

• Can CHI Software integrate cloud computing with our existing data science tools and workflows?

  Yes, we have such prior experience. Our experts have completed special educational programs through our partnership with Amazon Web Services and Microsoft Azure to help you integrate existing technologies.
  Additionally, we provide a variety of cloud-related services, from cloud migration to cloud-based application development.

• What are the first steps to move our data science operations to the cloud?

  To start, you need to conduct research. Think about what problems you want to solve and what features do you expect from your cloud services. Based on your business needs, choose a vendor and check the benefits they provide.
  After the initial research and choosing a vendor, it's time to think about what cloud platform you want to use. Next, you have to clean up your data. Lastly, apply best practices for cloud optimization.

• Why should you choose CHI Software for our cloud-based data science needs?

  We have helped numerous companies with cloud-based projects, as well as cloud migration for data science, and the number of satisfied customers keeps growing. Choosing us, you get:
  - Our expertise: The CHI Software development team is proficient in both cloud computing technologies and data science. Our developers have taken special educational programs from the leading cloud providers to offer our clients the best cloud-based solutions;
  - End-to-end support: From the moment we start working together to the end of the software’s lifecycle, we’ll be there with you. We can help your business implement data pipelines, integrate analytics tools, and use our extensive knowledge of machine learning platforms to bring you valuable insights from your data;
  - Guaranteed collaboration: To ensure the delivered software solutions align with your needs, we constantly communicate with our clients to meet their expectations. We go through all this to bring businesses solutions that meet their objectives and mitigate their challenges.