Being a significant industrial hub, businesses in Singapore continue to use data science to drive innovation. Data scientists utilize various cloud platforms to manage and deploy massive amounts of data and data-driven solutions. However, with so many Cloud platforms playing a central role in modern data science roadmaps, choosing the right platform is crucial to impact a data scientist’s efficiency and project success significantly.
From storing massive datasets to training and deploying machine learning (ML) models, Cloud platforms like AWS, Microsoft Azure, and Google Cloud dominate the market in Singapore. Hence, mastering even one of these Cloud platforms is crucial for data scientists aiming to stay competitive in their careers. This article explores various cloud computing for data science platforms to help you make an informed decision of choosing the best one, AWS, Azure, or GCP.
Your Success Starts with Learning: Cloud Computing Courses
Cloud Computing for Data Science: Comparing AWS, Azure, and Google Cloud
Cloud computing has become the foundation of modern data science by enabling data storage, processing, and machine learning. Among the top platforms, AWS, Azure, and Google Cloud offer powerful tools tailored for data-driven projects. Let’s compare some key services of the three platforms in the table below:
|
Service Type |
Azure |
Google CP |
AWS |
| File Storage | Avers vFXT, Files | Filestore | Elastic File System (EFS), FSx |
| Object Storage | Blob Storage | Cloud Storage Buckets | Simple Storage Service (S3) |
| Block Storage | Disk Storage | Persistent Disk | Elastic Block Storage |
| Data science/machine learning VM | Data Science Virtual Machines | Deep Learning VM | Deep Learning AMIs |
| Document capture | Form Recognizer | Document AI | Textract |
| Data Warehouse | Synapse Analytics | BigQuery | RedShift |
| NoSQL Document | Cosmos DB | Firestore, Firebase Realtime Database | DocumentDB |
| Domain Name System (DNS) | DNS | Cloud DNS | Route 53 |
| Virtual Machines | Virtual Machines | Compute Engine | Elastic Compute Cloud (EC2) |
| Content Delivery Network | Content Delivery Network | Cloud CDN and Media CDN | CloudFront |
| Security | Security | Security Command Center | Security Hub |
| Monitoring | Monitor, Anomaly Detector | Operations, Network Intelligence Center | CloudWatch, Transit Gateway Network Manager |
| Activity Tracking | Monitor Activity Log | Access Transparency and Access Approval | CloudTrail |
| Managed containers | Kubernetes Service, Container Apps | Kubernetes Engine | Elastic Kubernetes Service, Elastic Container Service |
Key Factors to Consider When Choosing a Cloud Platform for Data Science
Here are some key factors to consider when choosing a Data Science Cloud Computing Platform:
- Plan Ahead and Think Long-Term: Organizations often grow faster than expected, either through expansion or mergers and acquisitions. That’s why it’s smart to learn from past trends and plan for future needs accordingly. You may choose a cloud platform that can grow with you. For example, if your data needs doubled in the next three years, would your current platform be able to keep up?
- Prioritize Flexibility and Consistent Performance: Select a platform that strikes a balance between consistency and flexibility. Even if your needs stay the same, having options matters. Can it run on-premises, cloud, or hybrid setups? Does it support containers and Kubernetes? Are interfaces, licenses, and management tools consistent across deployments and easy to switch or combine?
- Ensure consistency across on-premises and cloud deployments: Many senior-level professionals prefer cloud solutions even for on-premises data. By using an object store as the primary data source, IT professionals can offer a seamless data experience, regardless of where the data is stored.
- Assess existing skills and available training resources: Verify that your team possesses the necessary skills for the chosen platform. This is one of the most critical factors for a successful setup. Consider training costs and how easily you can hire people who understand the platform. For example, you may have SQL experts, but do they know how to build new data models on your chosen system?
Additionally, consider the support and resources provided by external vendors. If your budget allows, consider consulting with experts who can assist with setup and training. Just keep in mind that cloud data analytics often requires new skills, such as DevOps, infrastructure as code, and CI/CD, for smooth operations.
5. Seek Customer Feedback: Look for customers who handle similar amounts of data and have similar analytics needs. Ask them in detail about the platform’s pros and cons and how well the vendor supported them during setup.
Also Read: A Beginner’s Guide to Programming
Choosing the Right Cloud Platform for Data Science: What Should You Consider?
Choosing the right cloud platform is key to enhancing your data science project experience. It’s essential to carefully evaluate options based on your skills, goals, business needs, available tools, and budget to find the best fit that supports growth and efficiency.
Here are some factors to evaluate the ideal platform for data science in cloud computing:
- Based on Skills: Choose a platform to match your technical skills and expertise and offer good learning resources for growth. A platform aligned with your skill set will speed up development and limit challenges.
- Based on Career Aspirations: Think about your future and where you want to take your career. If you aim to specialize in a specific tool, select a platform widely used in that area. Picking a platform that aligns with your goals can open doors to better job opportunities and growth.
- Based on Your Organizational/Business Requirements: The size, data volume, and security needs of your organization also play a crucial role in choosing the right Cloud platform. Ensure the cloud solution supports your business goals, offers scalability, and meets compliance standards to ensure seamless operations.
- Based on the Tools You Use: Check if the platform supports the data science tools you rely on, such as Python, R, or TensorFlow. Compatibility with your existing tools helps maintain workflow efficiency and avoids costly migrations.
- Based on your Budget: Cloud platforms vary widely in cost and pricing models. Choose one that fits your budget while still meeting your performance needs. Consider hidden costs, such as data transfer, storage, and training, to prevent unexpected expenses later.
Also Read: Data Science Bootcamps in Singapore
How upGrad Prepares You to Work with Leading Cloud Platforms
With upGrad’s Online Cloud Computing Courses, you will learn about the leading tools in cloud computing and related fields. These programs will help you harness the true potential of the cloud and elevate your skills to the highest possible level. Furthermore, upGrad’s Data Science and AI programs offer hands-on exposure to cloud environments, including AWS, Azure, and GCP, through real-world projects, labs, and expert mentorship.
Below are some programs to explore:
- Master of Science in Data Science, Liverpool John Moores University
- Post Graduate Diploma in Data Science (E-Learning), upGrad Institute
- Executive Diploma in Data Science and AI, IIIT Bangalore
- Post Graduate Certificate in Data Science & AI, IIIT Bangalore
FAQs on Which Cloud Platform Is Best for Data Scientists
Q: What is the best cloud platform for a data scientist?
Ans: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) are among the most well-regarded platforms for data scientists.
Q: Who pays better for cloud roles – AWS or Google Cloud?
Ans: A solution architect at AWS in Singapore gets an annual salary of SGD 2,06,400, whereas a solutions architect at Google Cloud in Singapore gets a wage of SGD 1,75,224. Both companies pay competitive salaries to deserving candidates.
Q: Can a beginner learn cloud computing for data science?
Ans: Yes, Data Science Cloud computing platforms are increasingly important for professionals, and there are many resources available to help beginners get started.
Q: How long does it take to master cloud computing for data science?
Ans: You can learn the basics of cloud computing in a short period of 3-6 months to help you become familiar with cloud platforms and start using cloud services. To master the program, it takes 1 to 2 years of focused effort.
Q: Can I access upGrad’s cloud projects without prior experience?
Ans: Yes, you can access upGrad’s cloud projects without prior experience. Some courses may have specific eligibility requirements. However, upGrad also offers various cloud computing courses specially designed for beginners.
