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Datawarehouse Augmentation
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ESolvit will add value to your data warehouse augmentation in building your existing data warehouse infrastructure, leveraging big data technologies to ‘augment’ its value. We will be able to leverage various types of data (structured, unstructured and streaming) generated from numerous internal and external sources.

Business challenges and ESolvit solutions:

  • » There’s too much of data in your data warehouse and you can't simply delete or tape archive some of the data – ESolvit will help you set up enterprise grade Hadoop product to offload data.
  • » To facilitate real time decision making there's a requirement to analyze or filter data that is continuously being generated – ESolvit will help you streamline computing
  • » You need a landing or holding zone for all your new data so it doesn’t fall on floor – ESolvit will help you augment your data warehouse with enterprise grade Haadoop

Your data warehouse needs augmentation if:

  • » You are drowning in very large data sets (terabytes, petabytes or more)?
  • » You use your warehouse environment as a repository for all of your data?
  • » You have significant amount of cold (low-touch) data that is not often being assessed?
  • » You are facing rising maintenance/licensing cost?
  • » You have to throw data away because you’re unable to store/process it?
  • » You want to perform analysis of data in motion to determine in real time what data should be  stored in the warehouse?
  • » You want to perform data exploration and navigation on complex and large amount of data?
  • » You are interested in using your data for traditional and new types of analytics?

Business benefits of augmenting your data warehouse:

  • » Organizations can reduce TCO, including licensing and maintenance costs
  • » Optimize the size and performance of data ware house by optimizing volume across data warehouse and big data technologies
  • » Reduce storage through smarter processing of streaming data
  • » Gain business insights by leveraging structured, semi-structured and unstructured data sources for deep analysis and by being able to analyze data in motion
  • » Significantly boost data warehouse performance
  • » Leverage more sophisticated analytical algorithms and enable new application or workload use cases

ESolvit will help your organization optimize your data as follows:

  • Metadata:  build information about inventories of big data
  • Data quality management:  cleans big data just as you conduct preventive maintenance on physical assets
  • Information life cycle management:  archive and retire big data when it no longer makes sense to retain these massive volumes
  • Big data governance:  ESolvit will help in establishing a strong big data governance in your organization which can be incorporated into existing information governance framework

ESolvit processes your data in 3 ways:

Staging environment:  ESolvit will set up a pre processing zone where enterprise grade Hadoop capability is used as a landing zone for data before determining what data should be moved to warehouse. Stream computing can also be as a real time component by processing and analyzing streaming data, without the need to store it first and determining what data should be saved – either in HDFS or data warehouse.
Advantages:

  • » Data can be cleansed and transformed before being loaded into warehouse
  • » Data need not be saved; being able to process and act on information as it is happening reduces storage in the warehouse

Query – able archive:  in this approach, cold data or aged data can be offloaded from the warehouse or application databases using information integration software and tools. Data can then be federated with warehouse data for federated queries.

Ad hoc analysis:  in this approach, analytics on data in motion can be done via stream computing. This enables organizations to perform analytics that were previously done in warehouses; this optimizes the warehouse and enables new types of analytics. Different data types (structured, unstructured and streaming) can be combined with ware house data, enabling deep analytics to provide insights not previously possible. Further stream computing can function as an analytics filter to find high value nuggets of data that then can be stored in data warehouse.

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