You don’t have to be a storage engineer, database architect or software expert to know there are dozens of back-end options readily accessible for applications these days. When was the last time you evaluated your back-end data architecture strategy?
Has your business considered what a non-relational backend component could do to facilitate specific functionality in your core line-of-business application ? In this blog, Eastwall shares a bit of insight in considerations that can be made when evaluating a non-relational architecture design.
It’s 2023 - if you haven’t at least considered what a non-relational datastore can do for your application, you are overdue! These solutions use a variety of data models, such as document, key-value, graph, and column-family, to store and manage data. Non-relational data stores are often designed to handle large amounts of data and can scale horizontally, making them well-suited for applications that need to handle high levels of data growth. They also provide more flexibility in terms of data modeling and can handle unstructured data. However, they may lack the ability to perform complex queries and may not have built-in support for transactions like relational data stores. Perhaps most importantly, NoSQL architectures and services can be deployed with a few clicks in the Azure portal - the time to investigate these options has never been better!
What’s the benefit of the non-relational datastore?
There are several benefits to using a non-relational data store, also known as a NoSQL data store, including:
Scalability: Non-relational data stores are designed to handle large amounts of data and can scale horizontally, making them well-suited for applications that need to handle high levels of traffic and data growth.
Flexibility: Non-relational data stores do not have a fixed schema, which allows for more flexible data modeling and the ability to handle unstructured data. This makes it easier to adapt to changing business requirements and data structures.
High performance: Non-relational data stores are often optimized for specific use cases and can provide high performance and low latency when compared to traditional relational data stores.
Cost-effective: Non-relational data stores often require less expensive hardware and can be more cost-effective than traditional relational data stores, particularly when dealing with large amounts of data.
Cloud-friendly: Many non-relational data stores are cloud-native, which allows for easy deployment and scalability in cloud environments. This can also reduce the cost and complexity of managing the infrastructure.
Real-time processing: Some NoSQL data stores like Cosmos DB, Azure Redis Cache, Azure Table Storage, and others are designed for real-time processing and low-latency data access.
Better for unstructured data: Many non-relational data stores are better suited for unstructured data, such as text, images, or video, which can be difficult to handle in traditional relational data stores.
Handling big data: Many non-relational data stores are built to handle big data workloads, which makes them ideal for use cases such as analytics, machine learning, IoT, and more.
But not without the pitfalls…
Despite the benefits, NoSQL is not a universal data problem solver. While these types of datastores can be useful in certain situations, there are also a number of pitfalls to be aware of before making the decision to use one.
Lack of built-in support for transactions: It can be difficult to ensure data consistency in certain situations. In a relational datastore, it is possible to perform a series of operations as a single transaction, which can be rolled back if something goes wrong. In a non-relational datastore, this is not always possible, which can make it difficult to ensure that data is consistent across multiple operations.
Lack of defined schema: With a traditional relational datastore, such as a SQL database, the schema is defined upfront and all data must adhere to that schema. This can make it easier to ensure data integrity and consistency. However, with a non-relational datastore, there is no predefined schema, which can lead to inconsistencies and errors in the data. This pitfall can be a trap that lures in unsuspecting developers and architects – looking for an easy-to-manage ecosystem then later finding that data structuring hinders business logic down the line. Ensuring your staff & peers understand these nuances in a non-relational ecosystem is key.
Scalability: While a ‘pro’ of the non-relational data ecosystem can be scalability, it can also be a detriment. While non-relational datastores can handle large amounts of data, they often require specialized tools and techniques to scale horizontally. This can be a significant challenge for organizations that need to handle large amounts of data and may be better served by a relational datastore that is designed to handle large amounts of data.
Bring Azure to the Backend
It's important to note that non-relational data stores are not a one-size-fits-all solution and each has its own strengths and weaknesses. It's always recommended to evaluate the specific requirements and use case of the application before choosing a data store solution. Azure offers several non-relational datastore platform services for storing and managing data. Some of the most popular options include:
Azure Cosmos DB: A globally distributed, multi-model database service that supports document, key-value, graph, and column-family data models. It is designed to handle high volume, low latency, and high throughput workloads.
Azure Table Storage: A NoSQL key-value store that is designed for highly scalable and performant storage of structured data. It is often used for storing large amounts of data, such as logs or sensor data.
Azure Redis Cache: An in-memory data store that can be used as a cache or a data store. It supports a wide range of data structures and is often used to improve the performance of applications by caching frequently accessed data.
Of course, other Azure services can be leveraged in a non-relational manner to provide similar functionality. Blob, Search, Data Lake and Queue Storage can all be architecture for scalable, loosely coupled technology patterns that support various processing requirements. Azure also supports a host of third-party vendor software to meet your non-relational needs.
It's important to consider the specific requirements and use case of the application to decide which service would be the best fit. Some of the services may not meet the requirement of the use case and some may be overkill. Even when the solution meets the need, being cost conscious is a major factor. Working with a trusted partner is key. Eastwall has worked with hundreds of organizations to evaluate current & future growth patterns, determine the appropriate backend architecture to support logical scaling & technology decisions that future-proof your line of business application for success. We help design, build, and operate innovative cloud solutions on the Azure platform. Please contact us for a free consultation on how various Azure cloud services can help transform your business.