Big Data analytic is the process of examining large set of data to understand patterns, correlation and business information. This involves logical approach in understanding the business decisions of organisations. Advanced analytic involve using software tools to find appropriate methods to increase revenues. Big Data analytic is the next future. A large number of customers are working on Big Data. There are certain practical examples that help to understand the working of big data. Let’s understand the practical examples:
Hadoop Big Data is not a new term for telecommunication industry. This sector has been collecting and storing huge volumes of call records over the past few years. Telecom switches are generating lots of CDRs every single minute. Hence terabytes of data is generated during the whole day that is 24 hour cycle. Hence there is a development in application that can easily pull data from CDRs and sensors. This has also helped in knowing the location and allocating bandwidth.
2014 was a year for Big Data Hadoop spark but 2015 has been estimated as an year of evolution for Big Data Hadoop. The industry experts feel there is a long way to go. For the year 2015 Apache Spark holds extended capabilities for Big Data Hadoop. These capabilities include more time based analysis. In the coming year we can witness new transcend for Big Data Hadoop. The Spark services shall be introduced along with the Spark packages.
Comparing the Hadoop installations for different organisations can be fun. The investment of a company can be inferred from the size of the installation. This also shows that big data products are bought from these vendors. Facebook has the maximum number of nodes for the Hadoop cluster as per the research study conducted during 2013. Yahoo and LinkedIn also have a large number of nodes. Hadoop clusters are also run in cloud for certain small organisations. I this article sizes are compared by number of nodes for the Hadoop clusters. They act as a specific data points for the same reason. The sizes can be compared with the help of CPU scores and data volume sizes.
The urge to move places has been the tendency from the past. Ancient tribes mobilized with the help of cave painting, their finger-marks, sounds and guidance from the stars. Now we use GPS to locate our location and reach our destination on various devices. Numerous videos have also been watched over places. This has led to lots of data generation for the phenomenon called Big Data.
A recent development in Hadoop technology is “Cloudera Director”. So Let’s see what is new in Hadoop. Hadoop has made a recent development with the most advanced and sophisticated for enterprise data hub and apache Hadoop. This feature extends the cloud architecture to the enterprise hub. Thus it has enhanced management and security feature. The standard for deployment is set by Cloud Manager by delivering granular visibility. Hence having control over every part of data hub. These results in high performance, enhanced security, reduced administrative costs, increased compliance.
Let’s understand what is machine language and how is it used? Machine learning is the self-learning method which involves generic algorithm. As we can understand these are generic in nature and can be applied into various domains of applications. Data Mining involves applying the algorithms to find solutions for domain related problems. It involves combining data with algorithms in this process. Statistics is a analytic study of collection, organizing and analyzing from the data. It deduces information from the data. Hence to learn self-developing algorithms machine language uses statistics and to solve a problem Data Mining uses results from algorithms with the help of statistics. In business domains data mining is used to solve complex problems. Continue reading What are the Differences between Data Mining ,Artificial Intelligence, Statistics and Machine Learning?
BiG Data! A Worldwide Problem?
According to Wikipedia, “Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
Continue reading Why Learn Big Data and Hadoop?