Despite all the hype related to NoSQL databases, Hadoop, and various other types of big data technologies, the importance of relational DBMS still remains the cornerstone of Information technology processing and infrastructure. For many decades now, relational DBs or SQL databases are being used to store, process, and manage data in every organization, including the global enterprise giants.
Organizations of varying sizes from less than $10 million in sales to $10 billion or more in revenue rely on relational databases’ capabilities for handling their enterprise data. However, if you cannot do the design part of the relational databases well, then your DBMS is at risk of failure to get all your valuable data fed into the system. There can be several adverse consequences of a failed database in terms of business operations and information technology processes.
One of the very basic considerations you have to make while designing the databases to meet all your performance expectations is what type of applications it will be supporting. When it comes to enterprise relational databases, we can consider them as two usage categories in order to plan and design and data modeling.
- Business workflow-related applications or transactional applications.
- Business intelligence-related applications.
Processes require data integration for information funneling to big data warehouses, data management hubs, and other complicated systems. Each of these data management categories can be supported effectively by using solid relational databases. However, the data modeling approaches may differ in order to optimize the database performance and throughput of your applications in the above categories.
Modeling data
From an enterprise database standpoint, the applications which are used for transactional purposes basically involve storing data, updating data, and retrieving data when needed. As a result of this, the structure of these types of enterprise databases should be designed in order to minimize, if not eliminate, any redundant records.
The goal here is to make sure that modifications and inserts of data are processed only once. This will help to boost and optimize the performance of the database as a whole. Doing this exercise will also help to avoid any inconsistencies in terms of data. This is accomplished through logical data modeling, which is usually in the third normal form for 3NF. In order to understand the normalization standard better, you can approach reliable consultants like RemoteDBA.com.
The business intelligence applications are maintained typically as read-only, where the users may query the data to be used for different analytical purposes. However, the query patterns and loads on the database may largely vary based on different use cases. When it comes to considering the best performance options for business intelligence systems, a dimensional relational database model is the best to consider. However, you must practice due diligence in order not to construct them very simplistically.
Applications for data integration are also essential to bridge business intelligence and transactional processes, which can be achieved using the best hybrid data models. These hybrid systems incorporate the elements of the third normal form, which will support data updates and layer on an advanced dimensional modeling technique. This can also use slowly changing methodologies in order to update information on the data warehouses alongside preserving complete historical records.
Moving on to physical database design
Finding an appropriate logical data model is the first step to establish a blueprint of a complete and functional relational DBMS design. Once if it is through; next, you need to make a physical data model which can describe the structural elements and entities to be put into an enterprise database. For this, you need to follow some design best practices. There may be variations in terms of syntax. Most importantly, according to the database technology you tend to use during implementation, the basic construction rules may also broadly vary.
Create your indexes, but do not overdo it
Indexes are widely used in order to speed up the processing of the select statement as database queries. In standard database conditions, columns that are used to select the rows within the given table or to join the given tables together are the basic things to be indexed. There should be a healthy amount of indexing out there when you are designing your database structure, but do not overdo it as the number of unique column values and rows in the table may get affected even when the indexes help improve the performance.
Leverage the benefits of partitioning
Indexes and data of the partition database tables may horizontally split into different entities. This is done based on some criteria like insertion dates as the values of the column. Different partitions schemes may vary largely between databases; however, partitioning’s typical benefits include quicker inserts, instant updates, and queries for easy management of large data tables. As there are different administrative tasks like rollbacks, back-ups, etc., are running simultaneously, it can be applied to all the tables’ subsets.
Leverage the built-in query optimizer features
Almost all standard RDBMS software may include a built-in tool for query performance optimization. You can examine an optimized execution plan for some major queries and can do the performance tuning accordingly. This will show you how the tables are joined and what types of indexes are used. With this information in hand, you can also restructure the queries for underlying table structures in order to boost the performance and get optimum output. This may be useful at times in order to provide some optimization in the queries, while the built-in optimizer needs some tweaks to ensure optimum performance.
Along with the tips mentioned above, there are also many low-level design tactics and technologies you can use to ensure more throughput from the relational DBs. There are options to favor the primary keys with the types of integer data over the character-based options. You need to explore what may work well for your organization and for the applications you use. Finding an apt relational database design will help you keep your entire corporate database-related tasks moving smoothly and make business executives and the end-users much easier.
