Analyzing big data without Hadoop
Big data is a term that describes the large volume of data (both structured and unstructured) that overwhelms a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Although the term “big data” may be relatively new, the act of gathering and storing large amounts of information for eventual analysis is ages old.
According to https://www.sas.com this concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three Vs: Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem but new technologies (such as Hadoop) have eased the burden.
Velocity. Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
Variety. Data comes in all types of formats ; from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.
Hadoop is an open-source framework for distributed storage and processing of large data sets. Consequently, there are fundamental paradigms Hadoop is based on, which seem not to be ideal for data measurement. Hadoop is limited to only one type of distributed processing: Map – Reduce programming model. This paradigm is ideal for row based data which you can find in business data. But for measuring data completely different paradigms promise faster solutions.
Hadoop consists of computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
Big data is mostly generated from social media websites, sensors, devices, video/audio, networks, log files and web, and much of it is generated in real time and on a very large scale. Big data analytics is the process of examining this large amount of different data types, or big data, in an effort to uncover hidden patterns, unknown correlations and other useful information.
Big data analysis allows market analysts, researchers and business users to develop deep insights from the available data, resulting in numerous business advantages.
