There is a great paradigm shift, in the way data information management systems are perceived. And rightly so, as there are massive amounts of data being created every day. The data is moving sinuously in many directions and people want to access that data from all over places. The old methods of integration have become unsuitable in today’s time, and demands.
The age of Big data demands big changes. Around 80% of data is unstructured, we get this data from different sources and enterprises. Data in Applications and Cloud is also growing at a constant pace with about 20% of data being touched by the ‘Cloud’. In some sense, we live in a matrix of data and information, with an ever increasing source base and target base.
This advancement opens the pathway to new and refined approaches. One cannot use technologies based on extract, transform and load methods (ETL), as it becomes a bottleneck. One would need a massive amount of custom coding to use these old world tools. Even then, we would be left with a list of challenges and time-consuming activities like lack of documentation, very limited reusability, dedicated manual hours, giving rise to increased cost with low productivity.
We now know, that modern data is complex and more distributed, and if we plan to take advantage of this data, we need to plug the incoming data to modern data integration.
So that you don’t waste time in fighting the tide, there are some Principles of Modern Data Integration which help us with some new insights from the massive data matrix that surrounds us.diploma-in-big-data-analytics
- Process data locally, place an agent locally, so the data is processed at the host platform before moving it. By doing this we can eliminate any bottlenecks.
- Modern data integration permits you to use powerful databases that already exist with built in functions to handle workflow, which can then be seamlessly blended, and distributed efficiently.
- Move data point-to-point rather than through data integration servers. Modern data integration allows you to move the data at the correct time and avoid bottlenecks. Also if you do not wish to move all the data, but to keep up with time, on certain occasions you can process data in the natural environment and only move the resulting base. So it removes the bottleneck, creates network space, and increases the speed in which data is saved.
- For accessibility, transparency, and reusability, it is advisable to manage all the business rules and data logic centrally. Modern day data platforms can be managed centrally, avoiding the chaos of dispersed integrated applications.
- Since modern data integration, through metadata repository, allows central management of all business rules and data logic templates, changes with new data or migratory data, across platforms can be done through the already available rules and logic, in an efficient and effective manner.
Therefore, it can be said, that modern data integration will eradicate many challenges that old data integration brought on. Moreover, it will create new capabilities and opportunities much beyond our expectation.