Big Data Applications Business Intelligence Applications Linear Scale Out Infrastructure for Big Data | DnA-IT

Platform for Big Data applications

  • Big Data Analytics

  • Big Data Infrastructure

  • Hyper Converged Architecture

  • The uniqueness of Nutanix

Transparent infrastructure for analytics and big-data applications

The field of analytics and Big Data occupies a central place in organizations and more and more organizations are transferring Big Data analytics projects to a state of production.

The increase in the volume of analytics requires an infrastructure and platform that will enable Scale-Up in an efficient and easy way, One Click Upgrade, the ability to control and monitor the health of the system online, a built-in option for analytics and predicting required resources in the future and more

 

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Along with the adoption of global use in the field of mobile and cloud, the field of big data, analytics and data processing occupies a central place in organizations. Recently, more and more organizations have moved big data analytics projects into production mode after realizing that they have the information they need to better understand their customers, gain better business insights and improve their business efficiency.

 

DnA-IT is the authorized and leading partner in Israel in implementing Nutanix infrastructure

Nutanix is leading the transparent infrastructure revolution

hadoop big data analytics splunk - a platform for big data analysis

Big data cloudera applications mongo db Analytics in Big Data Applications

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    What is the optimal architecture for Big Data applications?

    The field of Big Data analytics is growing

    Today, advanced analytics applications in big data analytics are producing real change in organizations. Organizations striving to gain a competitive advantage are investing heavily in this field and the trend is showing a clear sign of growth in the field. According to IDC organizations will invest a cumulative amount of $ 187 billion by 2019 – a 50% increase over the forecast given just 5 years ago. Whether it’s managing customer interactions, tracking online activity, or documenting information security mechanisms – these are all solutions that produce a large amount of information that is accessible to the organization and is called Big Data. The same Big Data is basically raw information generated from those systems which without smart analytical analysis supported by Machine Learning is worthless. Turning the data from the databases and infrastructure into business and / or practical insights is the real challenge. Finding trends such as security threats or new business opportunities from the data is a challenge that requires rethinking the application layer and also thinking about the corresponding computing and storage infrastructure. Big data and data analysis applications such as Splunk, Hadoop and more enable organizations to gather insights and opportunities from the data that is quickly stored. These workloads expand the limits of performance and require good continuous and random performance across the infrastructure alongside linearly growing computing resources.

    Big Data Infrastructure - Suitable infrastructure for Big Data applications

    The solution itself is built on the architecture of Scale Out – that is: all calculated and storage units work as one piece in order to analyze data in real time. Each computing unit also includes storage space and the information itself is replicated several times within the infrastructure cluster to maintain a high level of protection and provide fast access to data from each component in the solution. Depending on the types of solutions at the application level a collection of servers in a particular configuration is required to implement the solution. Choosing physical implementation without virtualization can make the solution operationally cumbersome in Data Center challenges such as: cabinet occupancy, electricity, cabling, hardware inventory management, hardware updates, network configuration, etc. Choosing to implement virtualization with traditional monolithic storage is not appropriate for this application because the information layout should match the amount of servers and maintain linearity.

    Hyper Converged architecture for Bid Data Analytics

    Hyper Converged architecture in the Web Scale concept is the optimal application for the Analytics application. This architecture makes it possible to reduce hardware, data center resources, simplify infrastructure and also maintain linear growth as needed.

    What is special about Nutanix's transparent infrastructure for analytics application?

    Nutanix’s transparent infrastructures provide linear infrastructure independent of specific hardware and independent of Hypervisor for linear growth realization also for analytics and big data applications. Many advantages can be noted in this architecture including:

    • Nutanix Hypervisor (AHV – which is provided free of charge as part of the infrastructure and includes impressive failure recovery capabilities, load balancing and added value for analytics application
    • One Click Upgrade – Built-in ability to upgrade the entire infrastructure at the touch of a button. The upgrade is performed while working and without interruption to the toilets
      And also includes hardware upgrades, network configuration and updating all infrastructure components
    • Health & Alerts – Ability to control and monitor the health of the array and relevant alerts regardless of external infrastructure components for the application
    • Cluster Expand – Ability to add resources at the click of a button. After installation in a cabinet, connection to electricity and a standard network, the system automatically detects computer and storage units that have been added to the infrastructure and offers the system administrator to attach them to the infrastructure. Clicking the OK button will make a logical addition to the entire array in less than 5 minutes
    • Analytics and forecasting – Built-in machine learning capability for analytics and forecasting the required resources in the future according to the level of use of resources in the past and present in the infrastructure
    • Extremely fast search and indexing capabilities – delivering up to 500,000 events / second for each infrastructure computing resource – is 2-3 times faster than competing solutions
    • Quick implementation – planning, installation and operation ready for implementation within a few hours
    • Easy and powerful HTML5 based Prism interface – easy and simple to use The Prism interface provides a central management application using the Single Pane of Glass method for all components and operations in the entire infrastructure
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