
Qrvey Cloud Native Analytics Platform > What is Cloud Native Analytics?
What is Cloud Native Analytics?
The term “cloud-native” refers to an approach to building and running analytics applications that takes full advantage of cloud computing. Cloud-native analytics platforms use design specifically for cloud environments like AWS, Azure and GPC. It leverages cloud services and infrastructure to achieve optimal performance, scalability, and resilience.
This approach enables organizations to handle large volumes of data, perform complex analytics, and generate insights faster than traditional on-premises methods.
Key Characteristics of Cloud-Native Applications

Microservices Architecture
Cloud-native applications are built using a microservices architecture, where the application is broken down into smaller, independent services that can be developed, deployed, and scaled independently.

Containerization
Cloud-native applications are typically packaged and deployed using containers, such as Docker, which provide a consistent and lightweight runtime environment across different infrastructures.

Automation and DevOps
Cloud-native development relies heavily on automation and DevOps practices. Continuous integration and continuous deployment (CI/CD) enable rapid and frequent application updates and deployments for developers.

Cloud-Native Services
Cloud-native applications leverage cloud-native services, such as managed databases, messaging queues, and serverless computing, to offload undifferentiated heavy lifting and focus on delivering business value.

Resilience and Scalability
Cloud-native applications are designed to be resilient and highly scalable, taking advantage of cloud infrastructure capabilities like auto-scaling, load balancing, and self-healing to ensure high availability and efficient resource utilization.

Declarative Configurations
Cloud-native applications often use declarative configuration files (e.g., Kubernetes manifests) to define the desired state of the application and its infrastructure, enabling automated deployment and management.
Benefits of Cloud-Native Architecture
In today’s SaaS macro-environment of ultra competitiveness, technology strategies play an important role. Some benefits of cloud native analytics are:

Increased Agility
Cloud-native architectures enable faster development and deployment cycles, allowing organizations to quickly respond to changing market demands and customer needs.

Scalability and Efficiency
Cloud-native applications can scale up or down automatically based on demand, optimizing resource utilization and reducing costs.

Resilience and Availability
The distributed nature of cloud-native architectures, combined with self-healing and load-balancing capabilities, ensures high availability and fault tolerance.

Portability and Vendor Independence
Cloud-native applications can be deployed across multiple cloud providers or on-premises environments, reducing vendor lock-in.

Cost Optimization
By leveraging cloud-native services and pay-as-you-go pricing models, organizations can optimize their costs and avoid overprovisioning resources.

Improved Developer Productivity
Cloud-native tools and practices, such as containerization and automated pipelines, streamline development workflows and reduce operational overhead.
What is the Difference Between Cloud-Enabled and Cloud-Native?
These terms are often used interchangeably, but there are differences.

Cloud-Native:
As mentioned earlier, cloud-native refers to applications and systems that are designed and architected from the ground up to fully leverage and take advantage of the cloud computing model.
Cloud-native applications are built using modern architectural patterns like microservices, containerization, and DevOps practices, and they are designed to be highly scalable, resilient, and available by leveraging cloud-native services and infrastructure.
Qrvey’s embedded cloud native analytics solution is built with container technology using on-demand cloud services. This provides a highly scalable solution with minimal infrastructure cost.

What Does Cloud-Enabled Mean?
Cloud-enabled refers to traditional applications or systems that are designed to take advantage of cloud services or infrastructure to some extent, but are not built specifically for the cloud from the ground up.
Cloud-enabled applications may use cloud services for certain aspects like data storage, backup, or content delivery, but their core architecture and design may still follow a more traditional, monolithic approach.
In the analytics space specifically, this is where most business intelligence system lie. They claim to be embedded cloud native BI, but in reality, they just run on rented cloud servers.
READ: Check out our guide to the best embedded BI tools.
The key differences between cloud-enabled and cloud-native applications are:

Architecture
Cloud-enabled analytics apps (AKA business intelligence) are often monolithic or have a more traditional architecture.
Cloud-native analytics (like Qrvey) are built using microservices and containers, with each service independently deployable and scalable.

Portability
Cloud-native SaaS applications are designed to be portable across different cloud providers or on-premises environments.
Cloud-enabled applications may have dependencies or tight coupling with specific cloud services or infrastructure.

Scalability and Resilience
Cloud-native applications are designed with scalability and resilience as core principles, leveraging cloud infrastructure capabilities like auto-scaling, load balancing, and self-healing.
Cloud-enabled applications may have limited scalability and resilience capabilities.

Automation and DevOps
Cloud-native embedded analytics relies on automation and DevOps practices like continuous integration and deployment (CI/CD).
Cloud-enabled applications may have more manual processes for deployment and management.

Utilization of Cloud Services
Cloud-native applications are built to fully leverage cloud-native services like managed databases, messaging queues, and serverless computing.
Cloud-enabled applications may use some cloud services, but their core functionality is often not tightly integrated with cloud-native services.
Cloud-Native SaaS Application Use Cases

Real-time Fraud Detection:
Financial institutions can use cloud-native analytics to process vast amounts of transaction data in real-time, identifying suspicious patterns and preventing fraudulent activities.

Customer Behavior Analysis:
E-commerce companies can analyze customer purchasing behavior, preferences, and interactions to personalize recommendations, optimize marketing campaigns, and improve customer satisfaction.

IoT Data Processing:
Organizations with IoT devices can collect and analyze sensor data from various sources to optimize operations, predict equipment failures, and develop new products or services.

Predictive Maintenance:
Manufacturing companies can use cloud-native analytics to analyze machine sensor data and predict equipment failures, preventing downtime and optimizing maintenance schedules.

Risk Assessment:
Insurance companies can assess risks, underwrite policies, and detect fraudulent claims by analyzing large datasets using cloud-native analytics platforms.

Harnessing the Power of The Cloud with Qrvey’s Cloud Native Analytics for SaaS Applications
As an embedded analytics solution that simplifies offering self-service analytics for SaaS companies, Qrvey was born cloud-native and takes advantage of cloud enabled machine learning and AI services that enable our customers to create embedded analytics applications in a self-service fashion.
We serve as an orchestration layer over these microservices that simplifies access and empowers a range of users (whether you are a data analyst, a data scientist or just a data enthusiast) to take advantage of them without having a dedicate engineering team with specific skills that would normally be required to create analytics applications.
When hosted on AWS, users can access Amazon Comprehend for Natural Language Processing (NLP) and text analysis to Amazon Rekognition for image and video analysis and Amazon Textract for text extraction from scanned documents to Amazon Sagemaker for machine learning model building and many more.
Our platform allows our customers to spend less time in development and more time delivering real-value to their customers.
With Qrvey, you can build less and deliver more.
Analytics for Those Who Want More
Build Less Software. Deliver More Value.
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