Big data is offering new opportunities for enterprises to score well in business. However, infrastructure support is also needed to make it possible, because in the absence of affective infrastructure, data silos is created, as seamless integration of data from all sources is not possible.
For instance, an online reseller might be able to review customer transactions, but may not have the browsing history of their customers, meaning they have only limited insight on customer behavior. This is where data analytics can experience a lag, as companies lack customer info spread across multiple applications, none of which are speaking to each other.
Here’s where converged architecture can help, as it assists in consolidating batch, transactional and streaming applications at one place, centralizing all or parts of your application patterns into a single platform to get a single version of truth.
Know how hyper converged infrastructure can help improve big data apps
Technically speaking, big data Analytics uses distributed computing architectures, on platforms like Hadoop, to run large processing jobs on gigantic data sets. Since, data being processed in a Hadoop environment usually needs to be resident on the compute nodes, it means that it needs to copy large files across the network for each job run.
Hyper Converged Infrastructure a derivative taken from Converged architecture has the potential to address the challenges of big data in storage environments. This architecture has computing function merged with storage and networking function along with server virtualization skills making entire infrastructure simple to set up and manage big data.
Thus, hyper converged architecture basically allows you to have everyone on your team improve data quality and categorization and ensures that the view you are getting on data is accurate and complete. For instance, marketing team and sales teams can enhance the data they use with certain attributes on a respective note, creating a platform enriched with data quality, relevance and depth for the organization as a whole.
On an additional note, by merging it into a centralized repository, one can have detailed segments on how data has changed, by whom, why, and for whom. This gives a holistic view of data and keeps the troubles of data silos at bay.
Finally, a hyper converged infrastructure would seem ideal to address big data related distributed computing challenges seen in environments such as Hadoop. It easily fits into all enterprise IT environments looking to gain value out of big data analytics.