Relational vs NoSQL – Top 10 App Requirements That Demand a New Stack
In order to successfully compete in today’s digital marketplace, businesses have to satisfy the ever-increasing data demands. In deciding which data management platform to build your application, you’ll need to factor in both data as well as your underlying application requirements. The choices are a traditional relational database management system (RDBMS) or NoSQL.
An RDBMS or a relational database stores data in a structured format, using rows and columns and follows the conditions of specific transactions. Apache Cassandra™ is an open-source, NoSQL database that is highly scalable and available. Cassandra's strength lies in its ability to handle a massive amount of data. Which one is right for your application?
IT analysts and investors say that NoSQL will dominate new software stack spending for years to come, especially given that they were designed for modern environments that span digital, mobile, and cloud applications that run everywhere. Today’s underlying application requirements, call for a modern data platform and for you to migrate from traditional databases, and oftentimes require a strategy that incorporates both legacy and new data management capabilities.
Relational vs NoSQL Requirements At a Glance
NoSQL and RDBMSs are designed to support different application requirements and can co-exist in most enterprises. The key decision points on when to use RDBMS or NoSQL technology for your applications include:
|
When to Use NoSQL |
When to Use an RDBMS |
|
Location independent / decentralized apps (e.g., web, mobile, and IoT) |
Location dependent / centralized apps (e.g., ERP) |
|
Continuous application availability |
High to moderate application availability |
|
High to low data/user activity |
Moderate to low data/user activity |
|
High to low online data volumes (e.g., requirement to retain data forever) |
Moderate to low online data volumes |
|
High-velocity data (devices, sensors, etc.) |
Moderate velocity data |
|
Semi/unstructured and structured data |
Primarily structured data |
|
Contextual transactions |
Traditional-only transactions |
|
Cloud-native database functionality |
Cloud-compatible database functionality |
|
Scale for both high volume reads and writes |
Scale for high volume reads |
|
Multi-data center, multi-cloud support |
No multi-directional data center or multi-cloud requirement |
DataStax Enterprise Makes It Easy
DataStax makes it easy to build, deploy, and scale modern applications that exploit the full value of ever-growing data. We built DataStax Enterprise (DSE) on Apache Cassandra™ one of the most popular open source databases with enterprise-proven scale, constant uptime, and elegant data distribution.
DataStax provides best-in-class support for modern application requirements that signal the need for a high-performance NoSQL data platform:
|
Application Requirement |
Supported |
DataStax Enterprise |
|
Location independence / Decentralized apps |
✔ |
Masterless architecture—the gold standard in data replication; accessible data any time—easily put data anywhere and synchronize it everywhere |
|
Continuous availability |
✔ |
Masterless architecture provides zero downtime—complete redundancy in data and compute resources |
|
High data/user activity |
✔ |
Linear scale and uniform response times via scale-out deployment |
|
High online data volumes |
✔ |
Linear scale capability via scale-out deployment |
|
Flexible schema |
✔ |
Semi/unstructured, structured data management; multi-model support for tabular, documents, key-value, and graph |
|
Contextual transactions |
✔ |
Integrated analytics, search, and in-memory engines for full contextual support |
|
Cloud native |
✔ |
Foundation built and proven by internet pioneers for cloud-native experience |
|
Scale for reads and writes |
✔ |
Low latency, lightning fast data loads, writes, and reads for current or future scale needs |
|
Multi-data center |
✔ |
Multi-directional, on-premises, hybrid cloud, multi-cloud, and intercloud support |
Approaches to Implementing with NoSQL
How do you approach moving to NoSQL and implementing your first application? In general, there are three ways to go about implementing with a NoSQL database:
- New Applications: Begin with NoSQL by choosing a new application and start from the ground up. Such an approach mitigates the issues of application rewrites, data migrations, etc.
- Augmentation: Augment an existing system by adding a NoSQL component to it. This often happens when applications have outgrown an RDBMS (e.g., due to scale problems, there’s a need for better availability, hybrid/cloud environments, etc.)
- Full Rip-Replace: For systems that simply are proving too costly from an RDBMS perspective to keep, or are failing due to increases of user concurrency, data velocity, or data volume, a full replacement is done with a NoSQL database.
If you are looking to implement NoSQL or want to learn about NoSQL and its benefits, below are some recommended resources:
