Data science and data engineering are incredibly resource intensive. Between accessing databases, using frameworks, using applications, and more, a lot of power is needed to run even the simplest algorithms. By using cloud computing, you can easily address a lot of these issues, as many data science cloud options have databases on the cloud that you can access without needing to tinker with your hardware. As such, here are a few data engineering and data science cloud options to make your life easier.
As one of the most popular data science cloud options, Microsoft Azure is designed for AI. With more than 200 products and services under its belt, and its speed, flexibility, and affordability options, Azure is popular for a reason. There are over 60 Azure regions available across well over 100 countries, making retrieval and real-time processing easy. Azure is also compatible with its massive library of other services as well.
Amazon Web Services
Aka AWS, Amazon Web Services often ranks as the most popular data science cloud option on many lists and job descriptions. AWS is also ideal for data scientists, as there are many other machine learning services and tools available to compliment its cloud offerings. What makes AWS special is its market share, range of databases, and flexible pricing.
Google Cloud Platform
Not to be overlooked by the leading two cloud platforms, Google Cloud Platform (GCP) has been climbing the popularity ranks in the last two years. What’s been helping GCP gain steam is its scalable nature, making it easy to run machine learning jobs quickly and easily by partly using GPU and TPU infrastructure.GCP also automates a lot of the monitoring and resource provisioning processes when running jobs.
While not as commonly used as the big three above, it’s started to enter the playing field since it acquired Red Hat a few years ago. For those loyal to IBM services, IBM Cloud has integration options for many Watson AI services, such as the IBM Cloud Pak for Data, Watson Machine Learning, Watson Assistant, and more. IBM excels with hybrid setups, so data scientists can work locally and virtually without disruption.
Lastly, Oracle Cloud is a viable option for some more niche applications. Many in business analytics and big data thanks to Oracle’s specialty in business experience and customer relationship management. Oracle also eliminates the need for other solutions, as it unifies many third-party services.
How to learn more about data science cloud options and data engineering
While all of the above data science cloud options have excellent tutorials online and robust online communities, nothing beats learning in a hands-on environment directly from the pros who use them. At ODSC East this May and ODSC Europe this June, you can check out the data engineering track for each event to learn more about these data science cloud options and more data engineering topics. Some highlighted sessions for each conference include:
- Winning The Room: Creating And Delivering An Effective Data-Driven Presentation
- Streamlining Your Streaming Analytics with Delta Lake & Rust
- CDC Stream Processing with Apache Flink
- Why dataframe is not always the best option for distributed computing
- Enabling Data Mesh With Even Driven Data Architecture
- Powering Millions of Real-time Decisions with Distributed Model Serving
- AI/ML, Edge Computing and 5G in Action: Anatomy of an Intelligent Agriculture Architecture!
- Data-curiosity: How to Create and Nurture a Data-durious Culture in your Organization
- The Power of AI in Aluminum Manufacturing
- Practical Pipelines: A Houseplant Alerting System with ksqlDB
- Google Cloud Big Data Essentials Workshop
- ML Governance: A Lean Approach
- Want End-to-End MLOps? Delta & Databricks Make This A Reality!
- How to build stunning Data Science Web applications in Python – Taipy Tutorial
- Bringing AI to Retail and Fast Food with Taipy’s Applications