Cloud analytics helps to overcome legacy data integration barriers by connecting data sources. Success of a global enterprise (be it an SME or a large corporate) is guaranteed only if a critical data analytics strategy is incorporated these days. Thankfully, with the emergence of big data or cloud-based analytics, it has become easier to deal with legacy systems in such organizations. However, maximum enterprises fail to pay due importance to the data integration plan and for instant results, they go in for ad-hoc approaches.
Legacy Data Integration on the cloud
- Cloud has been purpose-built to meet the demands of cloud integration. This means, there is no standard legacy component which can boost the speed of a legacy platform. That is why these components are powered with cloud analytics.
- Many organizations make use of self-upgrades to enable data streams that shuttle between files, databases, applications and other sources.
- Such execution networks can be configured to operate within Cloud. They can be made all the more robust with a firewall that would match the needs of systems and connected sources.
- Resources within the execution networks system are scaled up and down as per the data volume which is processed. Sometimes latency requirements of connected integration flows are also taken into consideration. As a result, both real-time and batch use cases are efficiently managed.
Some powerful integration clouds are rest-based which is why developers can embed the cloud hosting integration model into choicest platforms easily.
A few gaps still remain
Some of these approaches are not extremely efficient and sometimes they rarely work. Moreover, investment made in data analytics technology many a times gets overshadowed by BI (business intelligence) tools and applications. So the analytics methodology fails to see relevant data. In the end, just a fraction of the work gets done. This is a major reason why companies which leverage data analytics don’t rely completely on the result.
Common legacy data integration has thus become virtually instantaneous with the help of purpose-built cloud analytics. Yet, some gaps still remain in this technology. That is why enterprises need to thoroughly plan at a conceptual and physical level before carrying out data integration projects. Only then they can gauge the benefits of their big-data analytics strategy.