Top 5 Data Integration Patterns & Their Usefulness

Data Integration Patterns March 29, 2021

Mary Kate Morrow

Writer

5 Data Integration Patterns

Universally all business professionals know that data is a valuable asset for business processes and operations. Many people also have some knowledge regarding big data or other commonly used data management terminology. However, not all business professionals know how to actually utilize enterprise data correctly. Additionally, many people do not properly understand data integration nor its complexities.

Data integration patterns are an important component of data integration. Data intergration patterns supply a standardized method for integrating data. 5 data integration patterns that every business professional should know include-

1. Migration

Data migration is a data integration pattern that permanently moves a particular set of data from one system to another. Before data migration occurs, data is contained within a source system. The data migration process includes choosing, preparing, extracting, and transforming data. Migrations are widely discussed in data management case studies as migrations are necessary for all data systems. Data migration allows businesses to keep enterprise data when switching management information system tools. Migration capabilities are crucial as businesses often use multiple systems or different systems for data management purposes.

Extract transform load is often associated with the migration data integration pattern. Extract transform load and data migration are not the same although they are both important data management concepts. Extract transform load allows data to migrate between different sources and analysis tools. Data management business processes are critically influenced by both the migration data integration pattern and extract transform load.

Migration is a useful data integration pattern option for multiple situations. Migration is utilized most commonly when businesses move from one system to another system. Backing up datasets and replacing database hardware are also situations migration is useful for. Additionally, migration is helpful when adding nodes to database clusters or launching a new system extending current infrastructure.

2. Bi Directional Sync

The bi directional synch data integration pattern combines two data sets in two different systems. Bi directional synch allows two different data sets to exist separately while also acting like one data set. Bi directional synch data patterns help organizations with multiple systems and business processes occurring simultaneously.

Bi directional synch data patterns help eliminate the burden of businesses needing to manually address different data inconsistencies. Business processes are optimized by the high data quality and real time data accessibility that bi directional synch supports.

An example of bi directional synch could be a financial services firm that has different systems for different business processes. Bi directional synch would update different systems with the same real time data shared. With constant real time data accessibility, the financial services company could still keep systems specific for alternative business processes.

3. Correlation

The correlation data integration pattern incorporates bi directional synchronization. First, the correlation data integration pattern identifies where two data sets intersect. Then, bi directional synchronization is performed for the item occurring in both systems.

Importantly, bi directional synchronization only occurs if items exist in both systems naturally. Correlation avoids the need for unnecessary data storage as bi directional synchronization is only applied to the relevant intersecting data.

An example of correlation data integration could be between two different financial services locations. In this situation, correlation data integration patterns allow the separate locations to share customer data. When the different systems recognize the intersected data they then can use bi directional synchronization. As a result, both financial services locations are provided real time data access.

4. Aggregation

The aggregation data integration pattern receives or takes data from multiple systems and inserts it into one system. The aggregation data integration pattern maintains data quality and is an intergration solution for format issues. The ability to process data derived from multiple systems in a united application supports real time accessibility. Additionally, data replication is avoided which is important for organizations with limited data warehouse capacities.

Aggregation integration data patterns support the application programming interface used for legacy systems. Aggregation integration data patterns are especially useful for application programming interface that uses data from multiple systems for one response. Another useful application for aggregation integration data patterns is for enterprise data that is compliancy related.

An example of aggregation data integration pattern use could occur if a financial services organization needed to generate a report. However, the data needed to generate the support exists in multiple different systems. Aggregation data integration patterns help to generate the report with data from multiple systems united.

5. Broadcast

The broadcast data integration pattern moves data from a single source system in real time to multiple destination systems. The broadcast data integration pattern also occurs on an ongoing basis. Real time data access between multiple systems is only accomplished through broadcast, correlation, or bi directional synch data intergration patterns.

What makes the broadcast data integration pattern unique is that it only moves data in one direction. The one direction that data moves in during broadcast data integration patterns is from the source to the destination. As such, the broadcast data integration pattern is transactional in nature.

There are various considerations to help determine if the broadcast data integration pattern is the best choice for a situation. For example, consideration regarding if the need for real time data access is a priority. Additionally, the broadcast data integration pattern is great for situations that necessitate limited human involvement required.

Key Takeaways of Data Integration Patterns

Data integration patterns are an important topic for business intelligence related conversations.
5 data integration patterns include bi directional synch, migration, correlation, aggregation, and broadcast.