CIO OPINION
Once data is organised , silos can be eliminated by adhering to open standards , adopting open paradigms , offering choice , and avoiding lock-in to users , customers and operators alike . This helps the business firstly bring in all data types and secondly consume the data – or in this case , use the data to glean insights for improved business decision-making . This model also supports the API-centric IT economy as it ensures that data can be collected , no matter the source , because there is software interoperability via generic endpoints .
Maximally available infrastructure
Making infrastructure maximally calls for systems that can self-heal , dynamically adjust the write path to suit the workload and dynamically apply data transformation based on heuristics . When designing these environments , discipline is required because application placement and layout are important and need to factor in data sovereignty , workload affinity , workload availability and workload redundancy upfront . maximally available data architecture , this can be avoided entirely , and any upgrades to underlying infrastructure supporting your data are continuous without interruption to services . Now , adding capacity for storage or performance , or performing a software update , is seamless and non-disruptive to your data services .
Making data infrastructure invisible
A competitive data infrastructure is effectively invisible , trusted , and autonomous . It is trusted because it already takes into consideration data and application security , data protection , and data governance and compliance . It is autonomous because it brings together cloud-like simplicity and ease of use to database provisioning , management , and patching .
Traditional database management is done through an error-prone siloed approach to provisioning or cloning , which is time-consuming and makes
Maximally , available data is simple to manage through a central management plane that offers end-to-end capabilities around alerting , events and monitoring . It brings data closer to analytics – which is where it should be .
But what do we mean ? Let ’ s look at a traditional database environment where an application or database update may lead to issues across systems . Here DBAs take a snapshot before each update if they need to roll back to an ideal state . With a
By adding the tools needed to architect an invisible plane that can orchestrate the functions of your databases , your business can unify how data is stored and consumed with a data services platform .
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