Big Data is a key part of any enterprise technology for maximizing a company's competitive advantage and improving decision making. Hence, Big Data technology, including Apache Spark and MySQL, are in high demand nowadays. With the help of big data analytics and big data technologies, companies can analyze large sets of structured and unstructured data in real time and make intelligent and informed decisions. At present most businesses are leveraging computers and other internet-connected devices to collect, manipulate, analyze and act on data sets obtained from all over the world. In line with this trend, there has been an increase in the use of Hadoop framework and related tools. Big Data is a key part of any enterprise technology for maximizing a company's competitive advantage and improving decision making. Hence, Big Data technology, including Apache Spark and MySQL, are in high demand nowadays. With the help of big data analytics and big data technologies, companies can analyze large sets of structured and unstructured data in real time and make intelligent and informed decisions. At present most businesses are leveraging computers and other internet-connected devices to collect, manipulate, analyze and act on data sets obtained from all over the world. In line with this trend, there has been an increase in the use of Hadoop framework and related tools.
Hadoop is one of the two big data tools designed by the Apache foundation. The other tool is the Map-reduce framework. Apache Hadoop is based on the famous Map-reduce algorithm that was developed by Google and Yahoo. Map-reduce is one of the fastest data processing frameworks that are available today, used by several thousands of developers across the world. Map-reduce framework is comprised of four components:
HDFS is one of the key components of Hadoop. It is a relational database management system that is based on a key concept called "nesia" that makes it powerful in big data analytics software. The key feature of HDFS is its ability to efficiently manage, control and retain data. Additionally, HDFS also offers the capacity to scale up or down depending on the need. Also, HDFS offers very fast data access, which means application performance can be greatly improved.
While big data analytics tools like Hadoop are used to organize large sets of data, there are several key players in the development of Hadoop, like Yahoo!, IBM and Google that are driving the direction of its future. For instance, Yahoo! has invested billions into developing Hadoop, and recently, it acquired a major player in the market, Map-Reduce.
Map-Reduce is one of the key players in the Hadoop community. Map Reduce is written in Java, an open source language widely used for developing applications across the globe. In fact, Java is one of the top languages in use for developing real-time systems. The real-time capabilities of Map Reduce allow developers to efficiently manage multiple data sets, without having to concern themselves with managing their server.
Cloudera is another provider of big data tools. With its Hadoop distribution, Cloudera lets you easily scale your Hadoop cluster using a simple and easy-to-use interface. The distributed computing technology, also known as Clustered Map Reduce, makes it possible to efficiently utilize large and multiple data sets. Through Cloudera's HDFS, it is possible to create huge data sets that can be accessed by any user at any time.
Another tool for managing big data analysis is IBM's own Cognitive Computing Power Classic (CCP). Unlike most other tools available today, Cognitive Computing Power Classic (CCP) offers an approach to data warehousing, which focuses on real-time analytical processing. Cognitive Computing Power Classic integrates five major areas of data warehouse architecture: data specialization, central processing, data transformation, data management, and operational function execution. Data specialization deals with making the data processed from different sources into similar sources, while central processing and transformation handle the nuts and bolts of transferring data from one processing system to another. Data Management handles issues such as security, recovery, and integration of data with other processes such as marketing, manufacturing, and strategic planning.
The final big data analysis tool for managing real-time analytics is Hadoop, which is an open source framework based on the map-reduce. Google developed Hadoop because they needed a framework that could scale up quickly. The core of Hadoop is Apache Hadoop, an open source application server that is highly flexible and map/reduce-oriented. Hadoop can manage and collect large amounts of data, including text, images, and videos, from multiple data sources. Map-reduce is used to sort through this data to find the relevant data, then apply several different transformations to the data according to the business requirements.
