Learn to Integrate data management and visualization for better results in Hadoop

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Integrate data management and visualization for better results in hadoop

Integrate data management and visualization for better results in hadoop

Data management is an asset of hadoop
Hadoop is often considered as future of data management as this is the beauty of hadoop distributed file system that it manages the data in a much better and efficient manner. It simply means that hadoop is a complete package of data storage (often known as data management) and data processing (using map reduce programming paradigm). Further advancement of hadoop makes it a powerful framework and an extensible platform for both ongoing innovation in terms of data management and adoptions of enterprise application (using data visualization tools such as Datameer). In this article we will gain an insight into integrating data management with visualization techniques and how these techniques are helping the data scientist and researchers to automate and manage the data for better and advance predictions.

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Data visualization helping hadoop
Many of the data visualization tools are available in order to provide a detailed depiction of analyzed data and that too in an organized manner. These tools help the data analyst to have a better vision as compared to other traditional tools. Data visualization tool is pretty much helpful in discovery analytics, cohort analytics i.e. searching through billions of records within few seconds, filtering and sorting the records. Data visualization tools are also helpful in providing exploration services and with the help of self-service business intelligence we can use dashboards and customize portals in order to visualize the results. Hadoop is empowered with support for many of the data visualization API’s and libraries. These tools are also helpful in stream processing of big data (unstructured data). Some of the visualization techniques are also helpful in log data analytics i.e. real time monitoring of network activity and in depth analysis of semi structured and unstructured data. It can leverage the business intelligence by integrating relational databases with modern data sources, analyzing the data in petabytes and has the ability to copy the ETL data in and out just like real time historical processing of data. Transforming unstructured to semi structured data.

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What is the best suited technique for big data visualization?

Native Access support
Some of the tools that provide native support for Cloud-era Impala, Amazon red shift, and NoSQL databases like Mongo DB with search support for Apache solr and elastic search these are pretty much helpful in making and managing the data visualization.

How data Discovery is correlated with data visualization in hadoop
The prime focus of these tools is to have a reporting and monitoring the data management and data visualization as these techniques provide data analyst to intuit a way to sift through large volumes of data by replacing traditional data presentations with graphical depictions of pie charts. Data discovery tools have taken business to a leveraging position. Traditional stack vendors like Micro Strategy and SAP uses these versions and allow end users with some comfort level in data analysis to access multiple data sources. Data discovery mainly uses average user rating map that is known as likelihood to recommend overall satisfaction of users of software. With the product evaluation frequencies, the companies that are installing large data base center, experiencing significant growth and momentum are likely to be evaluated frequently.

Integrating data management and data visualization
The job to integrate a technology that has a dual feature of monitoring data management with data synchronization and data visualization was tedious, but efforts of many of the researchers and scientist made in possible to integrate the two. It is very helpful for many business insiders to have a tool that has features of both management and visualization because whenever we deal with a framework that has huge data without these features it won’t generate meaningful business. This results in a lack of vision, without which it will be hard for business leaders to make decisions and will be hard to find a good business intelligence platform. Data discovery with interactive visualization tools has emerged as an addressable solution to the problem of organized data management and visualization. Now a days, traditional data reporting has many tools that have Web Focused Business Intelligence feature and some of the salient features are-:

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Reduction in maintenance
Most of the shared data and centralized administration leads to security concerns and it greatly reduces the maintenance time and efforts of hadoop runtime applications (MAP REDUCE JOBS).    

Variations amongst different business intelligence systems
One of the business intelligence tool (InfoAssist) helps in enhancing data discovery capabilities and can be seamlessly appended with predictive analysis tools and performance management standard reporting as well.

Ability to access any type of enterprise data
With the help of data discovery tools, many users can operate data from the dashboard (also known as data discovery dashboard) that helps in deeper accessibility of data, ability to move the data rapidly and cover the critical phases of data extraction and data analysis.

Interactive dashboards for better data management
Data visualization tools use metadata for handling such a massive amount of data (usually for data management) and have an ability for incorporating existing reports with objects into dashboards, the technology has now turned from complex back end programming to high end user friendly data analysis and visualization tool. It also has an ability to integrate multiple tools (sharing of information for better analysis) and these tools can import data from other tools into same dashboard.

Provisioning the data for analysis
It is hard to tap multiple resources for dumping data collected from various resources and in order to have a centralized provisioning of data. Hadoop can be integrated with these modern visual reporting and data discovery tools to create aggregations as need arises.

Conclusion
This article focuses on the data management schemes and the ways in which the data can be integrated with visualization tools, in order to have a centralized data management and data visualization as well. There is a massive future scope for enhancement of data visualization integrated with complex hadoop application for a responsive predictive analysis.

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