Modeling is considered a ‘model-based testing tool’ which generates test inputs or test cases from stored information about a particular model. It also helps us to validate the tools and models of the system or software. For example, a tool can check the consistency of data objects in a database and can know the inconsistencies and defects. These can be difficult to pick up from the testing because we may have tested with only one data item and not realized that another part of the database contains conflicting information related to them. Modeling tools can be checking the state of models.
One advantage of modeling is, it is used by the developers and helps design the software. Both modeling tools and static analysis tools are used before dynamic tests can be run. This enables us to find and identify defects at a very early stage when it will be cheaper to correct them.
The characteristics are
- Recognizing inconsistencies and defects in the model
- It helps in identifying and prioritizing areas of the model for testing.
- To predict system response and behavior under different situations such as level of load.
Data modeling is very important step by step before developing a database application. It is rather very difficult to develop a database if we haven’t worked out its underlying structure first. How the data flows within the system, how the data is entered in individual tables, and what constraints apply to the data before storing them in databases.
How to select the data modeling tools?
- Usage and needs- This is our first deciding factor. How are we going to use it, how extensively, and what specific purpose? All data modeling tools are not created equally. Some have different areas of focus. our business requirements first to pick the right tool.
- Scalability- Our project might start small but grow in requirements. As we do not want to be ham-strong by an incapable modeling tool.
- Features- Once we have a grasp of the business requirements, the next step is to determine the right one among all the tools created, some are very specific.
- Integration- It is advised that some tools create a data model in a proprietary format and some use common or open file format, which is easily read into other tools.
- The user community- Many business tools have a user community that will turn into a community that can be helped.
The Data modeling tools are:
- Erwin Data Modeller
This data modeling tool is also known for being a more powerful but less expensive tool for businesses, than others and complies with the governance database rules. It is considered one of the best data modeling tools because it includes automated schema generation, cloud-based data solutions, and the power to create hybrid nature.
- Apache spark
If it is Apache it is known it works well with other applications like Apache products like Hadoop, it is good at handling large databases and parallel tests.
- Rapid Miner
It is ideal for those who will have never used any data modeling tool before because it is very easy to use. It can easily be incorporated through any data source type, including access, Teradata, excel, oracle, Microsoft SQL ingress, etc.
- Dbschema Pro
DBschema Pro is a powerful database design and also a graphical user interface tool for schema documentation. It also enables us to share and deploy schemas on relational and NoSQL Databases.
The main features are
- Independent of conceptual and also logical designs
- It allows the creation of queries with various ordering, grouping, filters, and joins
- it also creates test data using reverse regular expressions and specified patterns.
- It allows you to look at and change data from many varieties of tables by using foreign keys.
Questions
- Explain Erwin data modeler.
- Explain the Apache spark tool.
