HomeTechnologyBig Data Landscape and it's Tool: Complete Guide

Big Data Landscape and it’s Tool: Complete Guide

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Introduction

“Big Data” has become a major source of transformation across enterprises of all sizes.Having a lot of data is one thing, being able to store it, analyse it and visualise it in the real time environment is a whole different game. New 

technologies are collecting more data than ever; therefore many organisations are looking forward to better ways to make better use of their data. In a broader sense, organisations analysing  big data indoglobenews.co.id/en Tool Landscape need to view data management, analysis, and decision-making in terms of “industrialised” flows and processes rather than separate stocks of events or data. To handle these features of large quantities of data various open platforms had been developed.

Technology Transition

With the introduction of Big Data Tool Landscape has been a change in analytic techniques of organisations. The focus of the organisations has moved from orthodox methods like trend analysis and forecasting using historical data to its complementary and far better data visualisation techniques. More interests had been shown towards scenario simulation and development over standardised reporting techniques. Analytics is emerging as a key to enhance business processes.

Classification Of Big Data Tools

The Big Data tools landscape is rapidly growing and 

can be classified majorly into following area:

1. Data Analysis

2. Databases/Data warehousing

3. Operational 

4. Multi value Database

5. Business Intelligence

6. Data Mining

7. Key Value

8. Document Store

9. Graphs

10. Grid Solutions

11. Object Databases

12. Multi Model

13. XML databases

14. Big Data Search.

Big Data Landscape

In order to plan a big data indoglobenews.co.id/en architecture it is essential to have the knowledge of the big data landscape and incorporate it into existing infrastructure. In traditional data management structures, the data was fed into the enterprise integration tool which transferred the 

collected data into data warehouses or operational units. Then different analytical capabilities were used to reveal the data, but the new form of data management structures that inherit the big data landscape are designed to meet the velocity, volume, value and variety of requirements. To handle these big data sets, new architectures have been formed that contain multi node parallel processing techniques. 

Big data landscape has a further classification based on the processing requirements and different strategies are proposed for through which we can harness big data are :

1. Relational Database Management Systems

2. Massively Parallel Processing

3. MapReduce

4. NoSQL

5. Cassandra

6. Common Event Processing

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