Diving into The Ocean of Big Data & Analytics

  • Volume: Massive amounts of data being stored
  • Velocity: Speed at which data must be processed and analyzed
  • Variety: Different data sources and forms, such as numbers, text, video, images, audio, text, etc. from which data is gathered
  • Amazon EMR
  • Cloudera Data Platform
  • Google Cloud Dataproc
  • HPE Ezmeral Data Fabric
  • Microsoft Azure HDInsight
  • Data distribution and parallel processing: Breaking files into multiple small blocks (racks) stored in different units which are later processed and results are merged back together
  • Failure tolerance: Enabling big data solutions to scale up even on cheap commodity hardware rather than expensive SAN disks
  • Scalability & flexibility: Adding or removing racks from the cluster without worrying about the size for which this solution was designed
  • Cost-effectiveness: Lowering the cost of expensive servers with failure-resistant disks (e.g. SAN)
  • Prioritizing planned use cases and applications: Identity, structure, and prioritize the most promising cases and tasks using big data conceptual or data-driven approach.
  • Identifying new systems and tools needed: Recognize big data tools that can help you in handling huge business data sets and identifying patterns/ trends
  • Creating a deployment roadmap: Build a big data implementation roadmap as per your business goals, the maturity of your data management environment, and the amount of risk your organization can absorb
  • Evaluating internal skills: See if retraining/ hiring/ internal training is required to implement big data throughout your system

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Nitor Infotech Private Limited

Nitor Infotech Private Limited

Software product development services company that builds world-class products & solutions by combining cutting-edge technologies for web, Cloud, data & devices

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