hybrid integration connect to a back office ERP onpremises system .
Due to the complexity of back office systems , there isn ’ t yet a widespread SaaS solution that can serve as a replacement for ERP systems such as SAP R / 3 and Oracle EBS . Businesses should not try to integrate with every single object and table in these back office systems – but rather look to accomplish a few use cases really well so that their business can continue running , while benefiting from the agility of cloud .
Phase 3 : Hybrid data warehousing in the cloud Databases or data warehouses on a cloud platform are geared toward supporting data warehouse workloads ; low cost , rapid proof-of-value and ongoing data warehouse solutions . As the volume and variety of data grows , enterprises need to have a strategy to move their data from onpremises warehouses to newer , Big Data friendly cloud resources .
While they assess which Big Data protocols best serve their needs , they can start by trying to create a data lake in the cloud with a cloud based service such as Amazon Web Services ( AWS ) S3 or Microsoft Azure Blobs . These lakes can relieve cost pressures imposed by onpremises relational databases and act as ‘ demo areas ’, giving businesses the opportunity to process information using their Big Data protocol of choice and then transfer it into a cloud based data warehouse . Once enterprise data is held there , the business can enable self-service with data preparation tools , capable of organising and cleansing the data prior to analysis in the cloud .
Phase 4 : Real time analytics with streaming data Businesses today need insight at their fingertips in real time . In order to benefit commercially from real time analytics , they need an infrastructure to enable them with this level of rapid data insight . These infrastructure needs may change depending on the use case - whether it be to support weblogs , clickstream data , sensor data or database logs .
It ’ s best for IT leaders to first assess all their data sources in order to judge which ones must remain onpremises versus those that need to be moved to the cloud . For example , most IoT use cases involving sensors with industrial equipment are onpremises , so it ’ s best to keep your streaming analytics infrastructure on-premises . However , for use cases where you ’ re collecting streaming data about systems already in the cloud , it ’ s probably best to keep your infrastructure there also and use existing services within those ecosystems to set up your streaming infrastructure . That way you keep ahead of the game in terms of moving everything to the cloud .
Phase 5 : Machine learning delivers optimised App experiences We live in a ‘ mobile first ’ society , meaning that every experience will be delivered as an App through mobile devices . In providing the ability to discover patterns buried within data , machine learning has the potential to make applications more powerful and responsive . Well tuned algorithms allow value to be extracted from disparate data sources without the limits of human thinking and analysis . Businesses will need to harness the expertise of skilled developers who understand that machine learning offers the promise of applying business critical analytics to any application in order to accomplish everything from enhancing customer experience to serving up hyperpersonalised content .
Getting results with iPaaS In order for companies to reach this level of ‘ application nirvana ’, they will need to have first achieved or implemented each of the four previous phases of hybrid application integration .
That ’ s where we see a key role for integration Platform as a Service ( iPaaS ), which is defined by
The right iPaaS solution can help businesses achieve the necessary integration
Gartner as ‘ a suite of cloud services enabling development , execution and governance of integration flows connecting any combination of on-premises and cloud based processes , services , applications and data within individual or across multiple organisations ’.
The right iPaaS solution can help businesses achieve the necessary integration , and even bring in native Spark processing capabilities to drive real time analytics , allowing them to move through the phases outlined above and ultimately successfully complete stage five .
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