1. Best Practices for Managing Data Integration In the modern digital environment, businesses have to deal with  various real-time data streams as an integral part of data management  infrastructure. This can range from the more complex market trading data  to simple verticals like IOT readings, customer counters, and weather  readings. 

2. The term real-time though is relative – while  delays in weather updates or passenger counters are reasonably  permissible, the tolerance is much lower for an autonomous vehicle or  market trading app. However, the basic concept of real-time revolves  around creating models that can respond to constantly changing inputs  data, compared to the conventional batch-oriented data integration.  

3. Real-time  data integration is different from traditional data integration.  Enterprises need real-time data preparation technology to complement  conventional extract, transform and load (ETL) technologies. ETL can  help load context from corporate warehouses, ERP or customer  relationship management systems. On the other hand, real-time data integration can help add dynamic context to streaming data using emerging class edge computing architectures.

4. Here are some of the best practices that you should follow when adopting real-time data integration strategies.  Simulate and test the integration –  Real-time integration should be rigorously simulated and tested before  implementing it, unlike traditional data integration where algorithmic  trading desks would build a new algorithm for real-time data, test its  logic cursorily and start using it. If there is a bug, the consequences  can be disastrous. Update systems completely – It is not  advisable for organizations to use real-time to speed up existing manual  systems. Real-time should completely disrupt old batch-oriented ETL  applications. The focus should be on creating new kinds of value than  partly updating existing systems and procedures. 

5. Parallel processing –  The critical design approach in real-time data integration that should  be used for handling high-speed and high-volume streams of data is to  operate in a highly parallel fashion. This means making use of multiple  parallel and coordinated ingestion engines that are able to scale up or  down seamlessly to accommodate the requirements of data processing. The  breakthroughs in technologies for handling today’s high-speed data  streams have come about after recent innovations in parallel processing  and execution.  Avoiding component failure – A crucial  real-time data integration challenge is to tackle component failure in  some parts of the pipeline. If not properly designed, it can lead to  data loss, system outage, and stale or irrelevant data. The solution is  to decouple each phase of the pipeline and establish resiliency in each  phase so that the system as a whole runs smoothly. 

6. Package for better insights –  Real-time streams can only ensure business value when developers are  able to incorporate this data into new applications. Untapped data  streams can enrich business data but leads to poor information when  strategies are absent to pull actionable insights from the data.To  meet these challenges, businesses should have clear visibility into the  location of the data and the level of interaction between all  applications, systems and devices.