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Top 5 Data Integration Patterns

Data integration refers to consolidating data from different sources, making it useful for analyzing and getting business intelligence. Being crucial for data management, it incorporates data discovery, ingestion, mapping, cleansing, transforming, and delivery of data from varied sources. Integrating data into a specific format reduces its complexity and makes it more useful and easily accessible. It enables managers to gain valuable insights that help in taking crucial business decisions. Experienced data engineers of a leading data engineering company adopt various data integration patterns to standardize the overall process, based on the business needs and use cases.

Check out five data integration patterns that data engineers use to move, transform, and consolidate data and make it more valuable to businesses. 

  1. Aggregation
    Aggregation data integration pattern involves retrieving data from multiple sources, merging them into one system, and transforming them to provide a real-time view or make them useful for analysis. This data integration pattern is useful for APIs that use data from multiple systems to deliver a suitable response. Be it integrating enterprise data or customer data, aggregation pattern saves time and help meet the desired data analysis needs. 
  2. Broadcast
    Broadcast is the way data moves in one direction from a source to multiple destination systems continuously, without human intervention. It is one of the various ways to get data access in real-time from multiple systems. When it comes to processing data or records quickly, or getting valuable business insights from crucial data, broadcast data integration pattern is the most preferred. 
  3. Bi-Directional Synchronization
    When data engineers take two data sets from two different systems and merge them as one while both data sets exist independently, then it is called a Bi-directional synchronization data integration pattern. It ensures that the systems display a consistent, real-time view of the data to engineers and analysts. Organizations that require using different applications from various suites simultaneously, find this sort of integration pattern to be quite helpful.
  4. Correlation
    In correlation data integration pattern, experts of a leading data engineering company find out the point of intersection of two data sets. They perform bi-directional synchronization for the item that occurs in both systems. As bi-directional sync is performed only for the relevant data that occurs in both sets, it eliminates the need for unnecessary data storage, making the overall process simpler and more efficient.
  5. Migration
    Migration is a data integration pattern that refers to transferring a data set permanently from a source to a destination system. The process involves selecting, preparing, extracting, and transforming data into a standard format before it is replicated to a destination system. It is especially helpful for companies to maintain the integrity of their enterprises’ data when switching information system tools. As the data migration is independent of any platform, tool or system, it ensures effective processing of large data volumes and several records simultaneously.

Availing services of proficient data engineers is the best way to consolidate data from various databases and tap the power of this valuable business asset that would enable you to stay ahead of the competition.

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