An Android App Development Company Can Increase Data Processing Speed Using Hadoop BigData Solutions

Big Data is a collection of large data sets which are processed with a routine data processing method. It involves techniques, frameworks, and techniques for data involved in devices and applications. Some examples of Big Data for an Android app development company are:

  • Data From Social Media

It contains data from social media sites such as Twitter and Facebook store information that is displayed by people worldwide.

  • Data From Currency Exchange

This includes data and information for the various transaction carried out by clients for different companies.

  • Data From Power Grid

This contains datasets that have been used by a node in reference to a base station.

  • Data From Transport

Transport data include capacity, distance, availability of a vehicle.

  • Data From Search Engine

This can be used to recover data from databases that is present on various servers.

Types Of Big Data

Types of present in Big Data are:

  • Unstructured data- PDF, Word, Media Newspapers, Text
  • Structured data- Relational data
  • Semi-structured data- Data from XML Coding

Big Data Technologies

Different vendors like IBM, Microsoft, Amazon, etc. have various technologies that are used to manage large data volumes. Let us examine two major classes of Big Data technologies:

Operational Big Data

A system with operational capabilities that provide interactive workloads in real-time for data capturing and storage. MongoDB is an example of operational Big Data. NoSQL systems for big data are developed to utilize new architectures for cloud storage, This makes operational workloads for a large number of data much cheaper, faster and manageable for implementation.

Analytics For Big Data

Massively Parallel Processing(MPP) and MapReduce analytic systems are included in Big Data Analytics. These systems provide complex analysis which can affect all or most of the data. This will provide a new method for data analysis with SQL and MapReduce-based system scaled from a single server to thousand of high-end devices.

Problems Encountered By Big Data Are:

  • Presentation
  • Analysis
  • Share
  • Storage
  • Research
  • Data Capture

Big Data Solution From Hadoop vs. Old Coding Approach

Computers in traditional businesses will store and process large data. RDBMS like MS SQL Server, DB2 or Oracle Database is coded to interact with the required data that is present for users for analysis purposes.

This Approach is only valid if the volume of data is less used by database servers with reference to their processing limits. But while processing huge amounts of data traditional method for data processing can be very slow.

Google Has Introduced A New Solution To Tackle Slow Processing Of Data In Traditional Methods

Google has released a MapReduce algorithm to tackle this processing problem. In this solution the data processing task is divided among various computers in the network and result is collected in the form of a final dataset.

Hadoop Big Data Solution Is Also Used To Tackle The Slow Processing Speed Of Database Servers

Basically, Hadoop is an open framework that can process and store data within hardware clusters. Hadoop is developed to increase from a single server to thousands of local computers and storage. It gives a large amount of storage for any data type and a high-speed processing power and handles tasks virtually.

Hadoop Consist Of 4 Architecture Modules

1. Common Hadoop

It contains Java utilities and libraries that are been used by other Hadoop modules. These includes files and OS that are required to start Hadoop’s system.

2. Yarn Hadoop

This is used to start and manage cluster resources of the network.

3. Distributed File System

This provides broadband access to the application data of the server.

4. MapReduce For Hadoop

This is used for parallel processing for large data sets.

Advantages Of Hadoop

Hadoop store and process a huge amount of data at high-speed other advantages include:

  • Flexibility- Storage of data and processing of the same data can be done with users flexibility to use it.
  • Computing Power- Using a network of computers to process any amount of data, Hadoop has a high-speed computing power.
  • Low Cost- Hadoop is an open-source software system and it is free for storing any amount of data.
  • Scalable- Hadoop systems can be easily increased using simply by adding new nodes.


Top mobile app developers can utilize Big Data storage and processing capacities for large numbers of data by using Google solution or Hadoop data processing systems.

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