Nowadays, Big Data is everywhere. The term ‘Big Data’ refers to massive amounts of structured and unstructured data that are so big. Traditional data processing applications can’t handle them. The rapid development of technologies such as cloud services, Hadoop, NoSQL databases, and the Internet of Things (IoT) makes it possible to process all types of information in real-time.
The definition of Big Data has changed over time; initially, it referred to datasets whose size was beyond the ability of commonly used software tools to capture, store, manage, analyze or visualize (Kleiner et al., 2013). If you look at the original meaning closely, you will find no reference to the source of Big Data.
Big Data
Nowadays, Big Data refers to datasets whose size is beyond the ability of commonly used software tools to capture, store, manage, analyze or visualize. It also includes various data sources, such as social media and other online services, machine-to-machine (M2M) communications, and sensors from both mobile devices and non-computing “things” embedded in physical objects.
In addition, Big Data can be categorized according to its velocity, variety, and volume characteristics. In terms of velocity, Big Data means transient data created at high speeds by machines or users while they interact with other systems or applications. Thus an organization can’t process this kind of data using traditional technologies that run on a batch mode (such as Hadoop). According to variety, Big Data is machine-generated data in various formats such as texts, images, and videos. As for volume, the amount of data generated by modern systems can be very high.
The source of data plays an important role when we talk about Big Data. The four primary sources include structured data from relational databases; unstructured text from content management systems, blogs, and social media; semi-structured logs and events from network devices and transaction processing applications; and all kinds of sensor or telemetry data that come from machines or devices in a connected environment (for example IoT).
Changing trend of data
Since the early 2000s, when web search engines took off, companies such as Google, Yahoo, and Microsoft collected massive amounts of data. At that time, many people referred to this practice as ‘data mining.’ It made sense since machine-generated data contained useful information about individual users or company behavior. However, to apply advanced analytics techniques using machine learning algorithms, companies needed more than unstructured text—they also required metadata (e.g., date/time information) or statistical summaries of databases that would allow them to build models covering the entire customer base.
Today, Big Data means massive amounts of structured and unstructured data that so big traditional data processing applications can’t handle. There is a variety of new terminology related to Big Data. For example, ‘Big Data analytics’ means data processing to discover patterns and trends by applying advanced techniques using machine learning algorithms on vast datasets of structured and unstructured data.
Hadoop is an open-source software framework that supports storing vast amounts of data, running batch processing applications, and quickly accessing the results of queries. Its key features are reliable storage using commodity hardware, efficient processing by allowing batch jobs to run over multiple machines, the ability to handle any file or database structure, automatic failover to make it highly available, easy integration with other systems for loading information into Hadoop clusters or checking results of queries against other databases (with Apache Hive), etc.
Data Analysis
Companies such as Yahoo, Google, Facebook, Amazon, and Microsoft used Hadoop to analyze log data or click-stream records to find useful information in them according tyo RemoteDBA.com. Nowadays, they still use Hadoop to process unstructured text (and call it ‘text analytics’). However, this approach isn’t enough to derive business insights about customers’ behavior because of the variety of limitations related to machine learning techniques based on large datasets that cover only part of an organization’s customer base.
Another recently emerged tool is Spark—an open-source cluster computing framework for faster computation over large datasets by processing them in memory rather than storing them on hard disks. It’s often compared with MapReduce—the programming model used by all Big Data tools. However, Spark is more general because it supports many applications, including SQL queries and machine learning algorithms.
In addition, companies now use ‘data lakes’ as an alternative to Hadoop—a system that provides a central repository for all kinds of raw data. Analysts can access information from different sources in the same way they do it with traditional databases to derive insights from data. Data lakes are especially beneficial for companies planning to build complete business intelligence platforms by integrating their analytics assets (i.e., reports, dashboards, etc.). This approach makes sense because analysts need many different types of information about customers or users to construct valuable models for discovering insights—text processed using machine learning algorithms and metadata, statistical summaries of other internal data, etc.
One promising technology is unstructured data analysis that allows understanding the relationships between different categories or concepts encoded as text using natural language processing (NLP) techniques. For example, it’s possible to discover that a particular phrase means ‘new user’ by asking many people from various organizations what this term means and then building a model based on the results of these interviews. In addition, NLP makes it possible to find synonyms for words—by searching for them in different news sources. We can understand how journalists use them and select the most appropriate one for our purposes.
In summary, there are several recent trends in Big Data Analytics
– Transition from Hadoop to Spark
– Transition from data lakes to similar systems that provide an easy way to analyze unstructured text (e.g., by applying NLP techniques)
– Usage of machine learning algorithms based on large datasets covering only part of the population when deriving business insights about customers’ behavior
– Usage of NLP techniques to find relationships between different concepts encoded as text
Thanks to the wealth of Big Data, organizations can make better decisions by understanding what customers want or collaborating with many other companies simultaneously. However, they still have to deal with vast false positives because real insights are hidden in big piles of useless information. Organizations can avoid this issue by using tools that allow them to analyze the unstructured text without prior knowledge about machine learning algorithms—such approaches enable them to understand customer motivations more quickly and efficiently than ever before.
