How to Build a Growth-Focused Data Analytics Tech Stack in 2023
June 19, 2023
In 2023, building a growth-focused data analytics tech stack is all about cloud deployment flexibility and cloud-native support. According to Gartner, more than 85% of organizations will embrace a cloud-first principle by 2025, but they will not be able to fully execute their digital strategies unless they use cloud-native architectures and technologies. Cloud-native technologies empower organizations to build and run scalable data analytics in modern, dynamic environments such as public, private, and hybrid clouds.
Cloud Deployment Models
Your data analytics solution should support multi-cloud and hybrid cloud deployment models for greater flexibility, efficiency, and data protection. Here’s a brief overview of each model and its benefits:
Multi-Cloud simply means that a business is using several different public clouds such as AWS, Microsoft Azure, and Google Cloud, instead of just one. Why multi-cloud? Below are some of the compelling reasons:
- Being able to choose the best-fit technology for a cloud project.
- Getting the best value by choosing providers with the lowest cost and having leverage during price negotiations.
- Obtaining different geographic choices for cloud data center locations.
A hybrid cloud model uses a combination of public clouds, on-premises computing, and private clouds in your data center with orchestration among these platforms. Hybrid cloud deployment is useful for companies who can’t or do not want to make the shift to cloud-only architectures. For example, companies in highly regulated industries such as finance and healthcare may want to store sensitive data on-premises but still leverage elastic clouds for their advanced analytics. Other businesses may have applications that would require too much expensive movement of data to and from the cloud, making on-premises a more attractive option.
Beware; even though most analytics databases today run in the cloud, there are huge and significant differences between cloud-ready and cloud-native. Let’s explore what cloud-native means and its benefits.
The Cloud Native Computing Foundation defines cloud native as:
“Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds. Containers, service meshes, microservices, immutable infrastructure, and declarative APIs exemplify this approach.”
“These techniques enable loosely coupled systems that are resilient, manageable, and observable. Combined with robust automation, they allow engineers to make high-impact changes frequently and predictably with minimal toil.”
Below are some of the key benefits of a cloud-native analytics database versus a cloud-ready analytics database.
- Scalability: On-demand elastic scaling offers near-limitless scaling of computing, storage, and other resources.
- Resiliency: A cloud-native approach makes it possible for the cloud-native database to survive a system failure without losing data.
- Accessibility: Cloud-native uses distributed database technology to make the database easily accessible.
- Avoid Vendor Lock-In: Standards-based cloud-native services support portability across clouds.
- Business Agility: Small-footprint cloud-native applications are easier to develop, deploy, and iterate.
- Automation: Cloud-native databases support DevOps processes to enable automation and collaboration.
- Reduced Cost. A cloud native database allows you to pay-as-you-go and pay for only resources that you need.
Get Started with the Actian Data Platform
The Actian Data Platform provides data integration, data management, and data analytics services in a trusted and flexible platform. The Actian platform makes it easy to support multi-cloud and hybrid-cloud deployment and is designed to offer customers the full benefits of cloud-native technologies. It can quickly shrink or grow CPU capacity, memory, and storage resources as workload demands change. As user load increases, containerized servers are provisioned to match demand. Storage is provisioned independently from compute resources to support compute or storage-centric analytic workloads. Integration services can be scaled in line with the number of data sources and data volumes.
Start your free trial of the Actian Data Platform today!
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