DCN August 2016 - Page 31

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 ԁ)ѡѕɵ́٥)ٕѡѼѡՐ + ()A͔5)ɹ ٕ́ѥ͕)ɥ)]ٔaӊdͽ)ѡЁٕ䁕ɥݥ)ٕɕ́ ѡɽ՝)٥̸%ɽ٥ѡѼ)͍ٕȁѕɹ́ɥݥѡф)ɹ́ѡѕѥ)Ѽѥ́ɔݕəհ)ɕͥٔ]չɥѡ)܁مՔѼɅѕɽ)Ʌєфͽɍ́ݥѡЁѡ)́յѡ̸ͥ) ͕ͥ́ݥѼɹ́ѡ)ѥ͔ٕͭ́ݡ()չхѡЁɹ)́ѡɽ͔她)ͥ́ɥѥѥ́Ѽ)ѥɑȁѼ͠)ٕѡɽѽ)ɥѼ͕٥ͽ͕ѕи()ѥɕձ́ݥѠAL)%ɑȁȁ́Ѽɕ)ѡٕ́aѥمd)ѡݥѼٔЁٕ)ȁѕѡ)ȁɕ٥͕́́ɥ)ѥѕɅѥ)Qӊéݡɔݔ͕)ɽȁѕɅѥAљɴ́)M٥ALݡ́()QɥЁALͽѥ)͕ͥ́ٔѡͅ)ѕɅѥ()ѹȁ̃aեєՐ͕٥)ٕаᕍѥ)ٕɹѕɅѥ)́ѥ䁍ѥ)ɕ͕́Ր͕)ɽ͕̰͕٥̰ѥ)фݥѡ٥Յȁɽ)ձѥɝͅѥϊd)QɥЁALͽѥ)͕ͥ́ٔѡͅ)ѕɅѥٕɥѥٔ)Mɬɽͥѥ́Ѽɥٔ)ɕѥѥ̰ݥѡѼ)ٔѡɽ՝ѡ͕́ѱ)ٔձѥѕՍ͙ձ)єхٔ(((0