Big data is primarily defined by the volume of a data set. Big data sets are generally huge — measuring tens of terabytes — and sometimes crossing the threshold of petabytes. The term big data was preceded by very large databases (VLDBs) that were managed using database management systems (DBMS). Today, big data falls under three categories of data sets: structured, unstructured, and semi-structured.
Structured data sets are comprised of data that can be used in its original form to get results. Examples include relational data (i.e. employee salary records). Most modern computers and apps are programmed to generate structured data in preset formats to make it easier to process.
Unstructured data sets, on the other hand, are without proper formatting and alignment (i.e. human texts, Google search result outputs, etc.). These random collections of data sets require more processing power and time for conversion into structured data sets so that they can derive tangible results.
Semi-structured data sets are a combination of both structured and unstructured data. These data sets may have a proper structure yet lack defining elements for sorting and processing. Examples include RFID and XML data.
Big data processing requires a particular setup of physical and virtual machines to get results. The processing is done simultaneously to achieve results as quickly as possible. These days, big data processing techniques also include cloud computing and artificial intelligence. These technologies help in reducing manual inputs and oversight by automating many processes and tasks.
The evolving nature of big data has made it difficult to give it a commonly accepted definition. Data sets are consigned the big data status based on technologies and tools required for their processing.
Technologies and Tools for Big Data Analytics
Big data analytics is the process of extracting useful information by analyzing different types of big data sets. Big data analytics is used to discover hidden patterns, market trends, and consumer preferences for the benefit of organizational decision-making. There are several steps and technologies involved in big data analytics.
Data acquisition has two components: identification and collection of big data. Identification of big data is done by analyzing the two natural formats of data: born digital and born analog.
Born digital data: Information captured through a digital medium, such as a computer, smartphone app, etc. This type of data has an ever-expanding range since systems to collect different kinds of information from users. This data is traceable and can provide both personal and demographic business insights. Examples include cookies, web analytics, and GPS tracking.
Born analog data: Information in the form of pictures, videos, and other formats related to physical elements. This data requires conversion into digital format by using sensors such as cameras, voice recordings, digital assistants, etc. The increasing reach of technology has also raised the rate at which analog data is being converted or captured through digital mediums.
The second step in the data acquisition process is the collection and storage of data sets identified as big data. Since archaic DBMS techniques were inadequate for managing big data, a new process — MAD (magnetic, agile, and deep) — is used to collect and store big data. Since managing big data requires a significant amount of processing and storage capacity, creating such systems is out-of-reach for many. Thus, the most common solutions for big data processing today are based on two principles: distributed storage and massive parallel processing (MPP). Most high-end Hadoop platforms and specialty appliances use MPP configurations.
In-Memory Database Systems
These database storage systems are designed to overcome one of the major hurdles in the way of big data processing — the time taken by traditional databases to access and process information. IMDB systems store the data in the RAM of big data servers, drastically reducing the storage I/O gap. Apache Spark, VoltDB, NuoDB, and IBM solidDB are examples of IMDB systems.
Hybrid Data Storage and Processing Systems
Apache Hadoop is a hybrid data storage and processing system that provides scalability and speed at reasonable costs for mid and small-scale businesses. It uses a Hadoop Distributed File System (HDFS) to store large files across multiple systems known as cluster nodes and has a replication mechanism to ensure smooth operation even during individual node failures. Hadoop uses Google’s MapReduce parallel programming as its core. The name originates from mapping and reducing functional programming languages in its algorithm for big data processing. MapReduce increases the number of functional nodes over increasing the processing power of individual nodes. Hadoop runs on readily available hardware, which has sped up its popularity.
Data mining is a recent concept which is based on contextually analyzing big data sets to discover relationships between separate data items. The objective is to use a single data set for different purposes by different users. Data mining can reduce costs and increase revenues.