What is Data Analysis Platform?
An ecosystem of services for analyzing voluminous, complex data, enabling users to retrieve, combine, explore, and visualize data from various sources
Summary
- Incorporates predictive analytics, data visualization, location intelligence, natural language processing, and content analytics to turn all data types into actionable business insights
- Integrates different big data sources with transparent user access, manages and protects data assets through governance, and monitors system health and performance
- Enables data description, variable relationship identification, forecasting, cost reduction, and discovery of hidden markets and unfulfilled customer demands through comprehensive analysis
What is a Data Analysis Platform?
A data analytics platform is an ecosystem of services and technologies that needs to perform analysis on voluminous, complex and dynamic data that allows you to retrieve, combine, interact with, explore, and visualize data from the various sources a company might have. A comprehensive data analysis platform incorporates several tools with various capabilities, from predictive analytics and data visualization to location intelligence, natural language, and content analytics. Its main scope is to turn every kind of data into actionable insights for real business outcomes.
These platforms address the demands of users, especially those working with big data, on the inadequacy of relational database management systems (RDBMS) and enable organizations to make more-informed business decision.A comprehensive big data analysis platform should be able to:
- integrate different Big Data sources and provide a transparent view to the users;
- manage and protect the organization’s data assets in order to guarantee generally understandable, correct, complete and secure corporate data;
- monitor the data, resources, and applications to review and evaluate the health and performance of the whole system
A well-executed big data analysis, irrespective of whether the data is qualitative or quantitative, provides the possibility to:
- describe and summarise the data
- identify relationships between variables
- compare variables
- identify the difference between variables
- uncover hidden markets,
- discover unfulfilled customer demands
- discover unfulfilled customer demands and cost reduction opportunities
- forecast outcomes
- drive game-changing, significant improvements
Gartner®: Databricks Cloud Database Leader
If we are talking about big data Hadoop is the preferred choice for such a requirement mainly because proved to be reliable, flexible, economical, and a scalable solution. Even though Hadoop is capable of storing this large scale data on HDFS (Hadoop Distributed File System), it does not mean that it is the only solution available. There are multiple other tools available in the market for analyzing this huge data such as MapReduce, Pig and Hive.