What are the best practices for integrating RDBMS with other database technologies?

1 Identify the use case

Before integrating RDBMS with other database technologies, you should identify the use case and the requirements for the data. What kind of data do you need to store and access? How frequently and how fast do you need to read and write data? What kind of queries do you need to run and how complex are they? How much scalability and availability do you need? These questions will help you decide which database technologies are suitable for your needs, and how to integrate them with RDBMS.

2 Choose the integration strategy

Integrating RDBMS with other database technologies depends on the level of coupling and synchronization desired. Data federation, data replication, and data transformation are the common strategies employed. Data federation uses a middleware layer to access data from multiple sources without replicating or moving it. However, this may cause latency, complexity, and inconsistency issues if the data sources have different schemas or update frequencies. Data replication copies or synchronizes data in real time or in batches to have consistent and redundant data across databases. Though this may increase storage and network costs, as well as create challenges for data quality and integrity if the data sources have different formats or constraints. Data transformation converts or maps data from one source to another either on demand or in advance. This allows optimization of performance and functionality of each database but may require complex logic and create issues for data consistency and traceability if the data sources have different semantics or granularity.

3 Implement the integration solution

Once you have chosen the integration strategy, you need to implement the integration solution using the appropriate tools and techniques. To do this, you must design a data model for each database that is compatible and coherent with each other, using schema mapping, data conversion, or data validation techniques. Additionally, you must use the query language supported by each database, and ensure that they can communicate and exchange data with each other, which may require query translation, query optimization, or query federation techniques. Finally, you need to monitor and tune the performance of each database to handle the workload and expectations of users, which may involve indexing, caching, partitioning, or load balancing techniques.

4 Test and validate the integration solution

Once the integration solution has been implemented, it's important to test and validate it using the relevant metrics and criteria. Data quality should be verified to ensure accuracy, completeness, consistency, and timeliness across different databases, which may require data profiling, cleansing, reconciliation, or auditing techniques. Additionally, data security should be ensured through encryption, masking, access control, or auditing techniques. Finally, data governance should be established through policies, standards, lineage, or catalog techniques.

5 Review and update the integration solution

Finally, you need to review and update the integration solution as the data and the business needs change over time. This includes activities such as data maintenance, which may involve techniques like data refresh or data archiving. Data analysis can be done with data visualization or data mining. And data innovation may require data discovery or data testing. All of these activities help to provide insights and value for the business.