In a similar way, “unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics brings with it different requirements than storing data in a warehouse. How difficult is it to manage unfiltered data and get it ready for analytics?
In this paper, we’ll:
• Provide a detailed description of the weakness
• Show how it presents itself to the end user and the developer
• Explain mitigation strategies to help resolve each issue
With converged infrastructure (CI), that work is done ahead of time and behind the scenes. So it can be easier to install, deploy, update, and manage infrastructure, as well as to optimize its performance, minimize its cost, and maximize its business value. IT organizations can rescue skilled staff from “keeping the lights on” and focus them on realizing new technology-enabled business opportunities.
Although most application developers now consider agile a mainstream approach, database developers — especially those working on relational databases — have been slower to embrace it because of the need to understand and respect the state of a database when deploying changes. Thus, database professionals have had to rely on manual processes that do not scale up to the faster development cycles at the heart of agile.
The resulting challenges include:
• Ensuring that the code works properly
• Ensuring that the application functions not only today, but also years into the future
• Dealing with everyday setbacks, such as coding errors and rework, in the most effective way
• Making sure development projects are versioned properly