miniMAX Solutions is now helping businesses store, analyze, and
protect their data.

Transform your business with better predictions to understand customer behavior, optimize operations, manage risk, and enable innovation. Is your business prepared to leverage its data?

Turn your data into revenue

miniMAX provides the infrastructure you need to derive business outcomes from your Big Data. Address massive capacity while supporting traditional and next-gen applications by modernizing your data center.


Quick. Scalable. Integrated
Gaining computing power and Storage capacity by adding more Hadoop nodes without disruption by scaling out. Leverage massive capacity with no need for data migrations.


Massive. Centralized. Simplified.

Enterprise data growth is best addressed by consolidating storage into one central repository (Hadoop HDFS file system) with single volume architecture to simplify analytics and management no matter how large your data environment gets.


Trends. Gaps. Opportunities.

Whether descriptive, diagnostic, or predictive analytics, the greatest insights come from a powerful infrastructure – giving your company a competitive edge and increased revenue.

what is BigData?

Let’s move onto the definition and what we can do if we want to start with size. Some people care about size. And it’s important to realize that there’s no hard and fast definition of how much data you need in order for it to be considered big data. That said, if you take a look at a lot of the technologies out there and the workloads that they’re really designed to deal with and you take a look at the technologies that are not classified as big data, and the workloads they’re designed to work with, then you start to see that getting into the range of hundreds of terabytes that’s sort of defines the threshold wherein you really start to require big data technology and that the more conventional technologies don’t work quite as well.
It makes sense really at first to understand some of the scenarios where Big Data is produced. Big data has been discussed in mainstream media almost on the daily basis. It certainly stands to reason that it must exist, the question is, where is it coming from?
Now, certainly, web and internet scenarios including the analysis of web logs and clickstream data, that’s a canonical example that actually, most people are able to identify pretty quickly. Likewise,

  • Sentiment Analysis (Social Media)
  • Buying patterns
  • Fraud Detection; forensic analyses
  • Machine learning based investment strategies and iteration of same
  • Healthcare research (hospital can take advantage of big data analyses to determine how best to distribute services, help in research lab to understand genomic information)
  • Supply chain scenarios produce tons of data given the prevalence of RFID tags and the number of scanners in different supply chain facilities that scan those RFID tags and the articles that they are attached to.
  • Cell towers produce all kinds of data both about the calls that they connect and complete as well as just about the devices that pass near the towers and how long and what the signal strength is and what platforms and nominal numbers and brands names those devices are associated with.
  • Familiar names like Twitter, Facebook, LinkedIn etc.

All of these things are somewhat modern but we can go back even further. We can look at technology around supermarket check-out scanning. UPC codes which actually date back to the 1970s, they can produce Big Data too. And that underlies a really important point, which is that Big Data isn’t really new. We’ve always had it. What we haven’t done is keep it and analyze it. And what’s changing now is that we are keeping that data and doing analysis on it. So the question is what has given rise to that?

What is Hadoop?

Hadoop is an Apache project that combines a MapReduce engine with a distributed file system called HDFS, the Hadoop Distributed File System. Hadoop is the technology that is being used to implement big data.

Hadoop Common:


Hadoop Common refers to the collection of common utilities and libraries that support other Hadoop modules. It is considered as the base/core of the framework as it provides essential services and basic processes such as abstraction of the underlying operating system and its file system. Hadoop Common also contains the necessary Java Archive (JAR) files and scripts required to start Hadoop. The Hadoop Common package also provides source code and documentation, as well as a contribution section that includes different projects from the Hadoop Community.

Hadoop YARN:


Part of the core Hadoop project, YARN is the architectural center of Hadoop that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform, unlocking an entirely new approach to analytics.
YARN is the foundation of the new generation of Hadoop and is enabling organizations everywhere to realize modern data architecture. YARN is the prerequisite for Enterprise Hadoop, providing resource management and a central platform to deliver consistent operations, security, and data governance tools across Hadoop clusters.
YARN also extends the power of Hadoop to incumbent and new technologies found within the data center so that they can take advantage of cost effective, linear-scale storage and processing. It provides ISVs and developers a consistent framework for writing data access applications that run IN Hadoop.

Hadoop Distributed File System:

The Hadoop Distributed File System (HDFS) is the primary storage system used by Hadoop applications. HDFS is a distributed file system that provides high-performance access to data across Hadoop clusters. Like other Hadoop-related technologies, HDFS has become a key tool for managing pools of big data and supporting big data analytics applications.
The file system is designed to be highly fault-tolerant, however, by facilitating the rapid transfer of data between compute nodes and enabling Hadoop systems to continue running if a node fails. That decreases the risk of catastrophic failure, even in the event that numerous nodes fail.

Hadoop MapReduce:

MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner. MapReduce is a processing technique and a program model for distributed computing based on java.
The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.

Who uses Hadoop? And why?

Because hadoop has an ecosystem which resolves challenges of modern big data analyses.

  • Open source, free to start with
  • Uses Commodity Hardware
  • Fully Fault Tolerant
  • Scalable
  • Hadoop Distributed file System provides redundant storage of massive amount of data, replicated across Hadoop nodes in the form of small data blocks, it uses local disk in each node and present it as one big file system.
  • Hadoop MapReduce engine is a method for distributing computation across multiple nodes
  • Hadoop YARN is a resource-management platform responsible for managing computing resources in clusters and using them for scheduling of users’ applications, hence ensure right amount computational resources have been assigned to right task.
Tools for Hadoop

These tools allow programmers who are familiar with other programming styles to take advantage of the power of MapReduce


Hadoop processing with SQL


Hadoop processing with scripting.


Pipe and Filter processing model


Database model built on top of Hadoop


Designed for large scale data movement

Big Data Benefits for Telecom industry:

Big data offers telecom operators a real opportunity to gain a much more complete picture of their operations and their customers, and to further their innovation efforts. The industry as a whole spends far less on R&D than any other technology-oriented industry as a percentage of sales, and its efforts to change its ways have not yet proven broadly successful. Big data demands of every industry a very different and unconventional approach to business development. The operators that can incorporate new agile strategies into their organizational DNA fastest will gain a real competitive advantage over their slower rivals.
Big data promises to promote growth and increase efficiency and profitability across the entire telecom value chain. Following figure shows the benefits of big data over the opportunities available through traditional data warehousing technologies. They include:

  • Optimizing routing and quality of service by analysing network traffic in real time
  • Analysing call data records in real time to identify fraudulent behaviour immediately
  • Allowing call centre reps to flexibly and profitably modify subscriber calling plans immediately
  • Tailoring marketing campaigns to individual customers using location-based and social networking technologies
  • Using insights into customer behaviour and usage to develop new products and services
  • Big data can even open up new sources of revenue, such as selling insights about customers to third parties.
Two Approaches to Big Data in Telecom sector

Most big-data projects begin by defining a business problem to be solved, then trying to determine what data might solve it. These projects are run like traditional business intelligence programs, frequently achieving only incremental benefits.
The bottom-up approach begins with the available internal and external data, and allows out-of-the-box opportunities to emerge. Big-data pilots demand speed, agility, and constant iteration if they are to achieve really new and surprising opportunities.

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