Vertical scaling, also known as scaling up or scaling vertically, is a method of increasing the processing power and capacity of a single machine or server in a big data environment. This approach involves adding more resources to a single server, such as increasing CPU power, memory (RAM), storage capacity, or other hardware components, to handle larger workloads and data processing requirements.

Vertical scaling in big data involves enhancing the capabilities of a single server or machine to accommodate larger workloads and data processing requirements. While it can provide immediate performance improvements, it may not be a sustainable solution for handling extremely large-scale big data operations, which often require distributed computing and horizontal scaling to achieve the necessary scalability and fault tolerance. Apart from it by obtaining a Big Data Architect, you can advance your career in Big Data. With this course, you can demonstrate your expertise in the basics of Hadoop and Spark stack, Cassandra, Talend and Apache Kafka messaging systems, many more fundamental concepts, and many more.

In the context of big data, vertical scaling has the following theoretical characteristics:

1. **Hardware Upgrades**: Vertical scaling entails upgrading the existing server by replacing components with higher-capacity ones. For example, you might replace a dual-core CPU with a quad-core CPU, or increase RAM from 16GB to 64GB.

2. **Single-Node Focus**: Unlike horizontal scaling, where you add more machines to a cluster, vertical scaling focuses on enhancing the capabilities of a single machine. This means that all data processing and storage occur on a single, more powerful server.

3. **Cost and Complexity**: While vertical scaling can provide a short-term boost in performance, it has limitations in terms of scalability. There's a ceiling to how much a single machine can be upgraded, and at a certain point, further upgrades may become cost-prohibitive or technically challenging.

4. **Downtime**: Scaling up often requires downtime for the server to be upgraded. This can disrupt operations, so careful planning is necessary to minimize downtime.

5. **Use Cases**: Vertical scaling is commonly used in situations where an application or database requires more resources to meet increasing demands. It's suitable for scenarios where a single, powerful machine can handle the current workload and anticipated growth for some time.

6. **High Availability**: While vertical scaling can improve a server's performance, it doesn't inherently provide high availability or fault tolerance. To achieve high availability, redundancy, and fault tolerance, additional strategies like clustering or replication may be necessary.

7. **Software Considerations**: In addition to hardware upgrades, optimizing software configurations and applications to take full advantage of the upgraded hardware is crucial. It may involve adjusting parameters, tuning algorithms, or rewriting code.

8. **Limitations**: Vertical scaling has inherent limitations, and there will eventually be a point where further upgrades are not feasible. At this stage, horizontal scaling (adding more machines to a cluster) becomes a more viable approach to handle increasing workloads.