From improving the accuracy of predictive models to helping manage complex supply chains, data-intensive applications are a catalyst for innovation and growth in modern business. In this article, we’ll review the different kinds of data applications and their capabilities. We’ll also delve into the core components required to successfully build, deploy and maintain these apps.
Types of data-intensive applications models
Data-intensive apps can be categorized into three types: managed, connected and Snowflake Native. Let’s look at each to understand their similarities, differences and the use cases each is best suited for.
Managed applications
The managed application model is the traditional approach to developing applications. It is delivered as a fully managed service, with the software provider ingesting customers’ data and processing it using their own highly performant data platform. Traditionally managed apps provide software companies with flexibility in design and deployment and can help streamline onboarding and billing.
Although a managed approach works well for some companies, if sensitive or highly regulated data is involved, this model can create an uncomfortable level of risk; data owners are unable to manage data stored and processed on the provider’s platform. As consumer and regulatory scrutiny around data security grows, many businesses are rethinking this approach.
Connected applications
Connected applications resolve many data security concerns accompanying traditional managed applications by decoupling app code and customer data. In the connected model, the software provider creates and maintains the application code while customers manage their own data using their external data platform to process the application’s data. By connecting to an external data platform, connected apps eliminate the need for sensitive data to be entrusted to a third party. Software providers also benefit since they can significantly reduce their data storage and processing costs.
Native apps
Apps that run entirely within a customer’s data platform are built, distributed and deployed in the cloud. Unlike connected apps, these apps bring the application’s code directly to the customers’ data. For example, with Snowflake’s Native App Framework, developers can build apps within Snowflake and deploy them directly to their customers’ data stored in their Data Cloud.
This model provides significant advantages for customers, allowing them to discover and install apps within minutes. By running apps within their Snowflake instances, customers improve security and governance. With the app code and data all handled within Snowflake, customers can launch extended collaboration efforts using global data clean room apps and leverage other innovative technologies.
Essential ingredients for developing data-intensive applications
Data-intensive applications demand significant resources. Having the right infrastructure to support them will make the difference between success and failure. Here are five must-have components for supporting modern data applications.
Limitless scalability for compute and storage
With data-intensive applications, a spike in the number of users, the amount of data they use or the number of queries they perform can strain infrastructure that’s not automatically scalable. To function seamlessly, data-intensive applications must separate compute and storage so it’s possible to independently scale each component. Scalability should be automatic and without limits, with apps free to instantly and infinitely scale compute up, down and out to accommodate all workloads without impacting performance. Resource provisioning should be immediate, allowing users to dynamically match their resources to usage demands, independent of data volume.
Low latency
Latency is the measure of time from when data is generated to when it becomes available for storage or retrieval. Many data-intensive applications require ultra-low latency to access data in near real time. Low latency not only provides a superior user experience, but it’s also a necessity for running advanced, data-dependent technologies such as predictive analytics programs and cybersecurity platforms. Low latency requires data concurrency, so multiple users can work with data simultaneously through multiple compute clusters, eliminating slowdowns and disruptions.
Support for rich data
Due to the variety of data sources most applications must work with, an integrated approach is essential for handling relational, unstructured and semi-structured data such as JSON, Avro, Parquet and XML.
Fully managed service
By leveraging a fully managed platform, software companies can direct their resources toward developing their product instead of maintaining the infrastructure that supports it. With a fully managed service, there’s no infrastructure to manage or optimize, and software updates can be carried out without experiencing any downtime.
Robust security and governance
Many apps process and store sensitive data. Selecting a cloud platform with robust security and governance tools helps businesses ensure that the sensitive data maintained by the organization is protected from inappropriate access. It also makes achieving and maintaining regulatory compliance easier, protecting the company from financial, reputational and legal risk.
Build Your Data-Intensive Applications on Snowflake
With industry-leading scalability, performance and flexibility, Snowflake has the resources required for developing and scaling data-intensive applications without operational burden. Snowflake helps accelerate product velocity by making it easier for developers to build, test and deploy data-intensive applications. With the Snowflake Native App Framework, developers can build applications within Snowflake and deploy them directly to their customers’ data stored in their Data Cloud. Additionally, Snowflake leverages the most sophisticated cloud security technologies available. The result is a service that is secure and resilient, giving you the confidence to enable your most demanding data workloads with Snowflake.