Analyzing big data without Hadoop

What is big data?

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 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.

What is Hadoop?

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.

Analyzing big data with Hadoop

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. Business users are able to make a precise analysis of the data and the key early indicators from this analysis can mean fortunes for the business.

As a result of improved innovations and regular evolutions of technologies, some systems and companies have data files measured in petabytes or yottabytes. Since data is being generated, measured and stored in very large volumes with great velocity in all multi-structured layouts like images, videos, blogs, sensor data, etc. from all different sources, there is an enormous demand to efficiently and effectively store, process and analyze this large amount of data to make it operational and functional.

Hadoop is undoubtedly the preferred choice for such a requirement due to its key characteristics of being reliable, flexible, economical, and a scalable solution. It provides the ability to store huge data on the HDFS – Hadoop Distributed File System. Moreover, there are other solutions available in the market for analyzing big data, these other solutions have led to the rise of many different school of thoughts about which Hadoop data analysis technology should be used when and which could be much more efficient.

A well-executed big data analysis provides the possibility to uncover hidden markets, discover unfulfilled customer demands, cost reduction opportunities and drive game-changing, significant improvements in everything from telecommunication efficiencies and surgical or medical treatments, to social media campaigns and related digital marketing promotions.

Analyzing big data without Hadoop

Hadoop, even with its high-level efficiency exhibits some shortcomings. These features includes; fragmented data security issues, lacking of tools for data quality and standardization and finding programmers who have sufficient Java skills to be productive with Map-Reduce.

As a result of large amounts of data files measured in petabytes or yottabytes, there will be instances that require the fast response times of dozens of machines in a Hadoop cloud running in parallel, but also, plugging along on a single machine without the hassles of coordination or communication will be much more effective.

Besides, companies have data sets that can easily fit into the RAM of a basic PC. In most algorithms, the data doesn’t need to be read into memory because streaming it from an SSD is fine. Also, there are multiple solutions available in the market for analyzing this huge data like Map-Reduce, Pig, CERN and Hive but the most recent is Spark.

Spark borrows some of the best ideas of Hadoop’s approach to extracting meaning from huge volumes of data and updates them with a few solid improvements and enhancements that make the codes run much faster. Spark keeps data in fast memory instead of requiring everything be written to the distributed file system. CERN also processes 90% more data than Hadoop using its custom frameworks.

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