Big Data: Features, Technologies, Users

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Business growth and development require information. However, the exponential growth of a firm depends on the marketing strategy. A firms marketing strategy is a product of analysis and decision-making. Thus, information facilitates a firms exponential growth. Data analysts revealed that billion bytes of data are created daily. The exponential increase in the quantity of data created influences the decision-making process in many organizations (Olofson, 2012). The data created include videos, traffic information, pictures, climate signal, and business information to mention a few. Thus, the information gathered is called Big Data.

Big Data is a term that describes the continuous growth of structured and unstructured information. Unstructured data is a generic term that describes unmatched data. For example, email messages, video files, picture files, word documents. Structured data are information stored in a database. As a result, business executives can utilize the billion bytes of data to make and manage business decisions. The benefits of Big Data can be summarized in four points. First, Big Data make information reliable and valid. Second, Big Data improve a firms business decision. Third, Big Data transform business and marketing strategy to support exponential growth. Forth, business transformation will increase client base and reduce costs. Features of Big Data include velocity, variety, volume, variability, and complexity (Olofson, 2012).

Variety. Big Data contain structured and unstructured information. Structured information is stored in a fixed location. Structured data include business models, data warehouses, enterprise systems, and XML data. Unstructured data are not stored in a fixed location. For example, audio files, video files, RSS feeds, email messages, power point presentations and excel spreadsheets.
Volume. The volume of Big Data grows exponentially. Structured and unstructured data are streamed daily. Thus, data volume is a feature of Big Data.
Velocity. Billions of signals and data are ingested and transmitted daily. However, business organizations encounter challenges in managing massive torrent data.

The importance of Big Data

It is true the billion bytes of data are created daily. However, what we can do with the data is the focus. Business organization can manage cost with Big Data. Downtime reduction is another benefit of Big Data. Big Data can influence smarter marketing decisions. Finally, business organization can forecast customers needs to ensure sustainability and satisfaction. For example, the ford explorer car transmits the massive amount of data while in motion and at rest. While in motion, the electric car provides sensory data to the driver. The sensory data include fuel level, acceleration level, tire pressure, and battery temperature. Consequently, the electric car transmits data to the manufacturer (Oracle, 2012). The information includes the customers driving speed, and maintenance habit. At rest, the electric car transmits battery power, tire pressure, car temperature, oil level, and temperature.

Tools /technologies used to accomplish Big Data

The business of data collection is influenced by different factors. Processing time, storage capability, scale-out technique, and data visualization pattern are features of Big Data tools (Dawson, 2010).

Hadoop Apache

Hadoop is an instrument that supports the distribution of trillion bytes of data to server terminals and computers. The technology utilizes simple programing architecture to scale-out massive amount of data. Doug Cutting made Apache Hadoop from a user-function designed by Google. Hadoop transmits Exabyte across multiple parallel nodes. Business organizations can use Hadoop cluster at reduced cost. As a result, companies can target large scale-out projects using the Hadoop Apache. Thus, Hadoop technology splits Big Data using simple models.

How it works

When a customer accesses unstructured data using Hadoop tool, it splits the semi-structured data into multiple parts. The processed data is transferred to system nodes in parallel. Trillion bytes of unstructured data are stored in the Hadoop distributed file system. The Hadoop file system replicates the unstructured data to withstand data failure. However, the Hadoop facilitator transmits information from the file system. A name node describes communication sensors, available nodes, and data location. Consequently, the loaded data cluster is transferred to the MapReduce for analysis. When the client submits a job for analysis, the job trackers send the name node to access relevant files. Once the information is complete, the job tracker returns the query to suitable nodes in parallel. The processed data is transferred from the MapReduce to Hadoop storage system. When the node completes the job, it transfers the result to the database. Thus, unstructured data analysis can be shared into multiple parts using the Hadoop technology.

NoSQL utilizes simple design models to retrieve trillion bytes of unstructured data. Features of NoSQL architecture include control, design, and horizontal scaling. NoSQL retrieves Big Data from various storehouses. NoSQL databases include HBase, CouchDB, Riak, Aerospike, MarkLogic and DynamoDB. However, NoSQL database has compliance problems. As a result, customers combine NoSQL with Hadoop systems.

Parallel Analytic Databases

The architecture stores massive amounts of data using scale-out nodes. Unlike the NoSQL databases, Parallel Analytic Databases ingest massive SQL queries. The interactive capability is a notable feature missing in Hadoop system. Some features of Parallel Analytic Databases include columnar architecture, advance compression ability, memory processing, and shared-nothing mechanism.

Big Data users

Companies that use Big Data architecture include General Electric, KAGGLE, Ayasdi, IBM, Weather Company, Mount Sani Icahn School of Medicine, KNEWTON, SPLUNK, GNIP, and EVOLV. General Electric uses Big Data to support customers data. For example, GE made a parallel node that transfers airline data into cloud databases. KAGGLE employed 140,000 data analysts that recommend marketing solutions for business organizations (Oracle, 2012).

The organization utilizes Big Data resources to solve clients query. For example, KAGGEL developed numerous algorithms for Amazon, Google, and Facebook. Thus, KAGGEL team of scientist offers business solutions. AYASDI uses Big Data to track and retrieved sensory signals from different databases. The company utilizes virtual approach to create 3-D maps (Dawson, 2010). For example, AYASDI monitored the source of a bacterial outbreak using a 3-D map. IBM shares business ideas and models using Big Data architecture. Weather Company uses Big Data to forecast climate change. KNEWTON supports students e-learning using Big Data architecture. GNIP uses Big Data sensors to stream files on social media sites. For example, Instagram, Google Plus, and You Tube share Big Data architecture. EVOLV uses Big Data architecture to recruit company workers.

Conclusions

Big Data application supports exponential growth. The architecture ingests a massive amount of structured and unstructured data. As a result, many organizations can render various services at reduced cost. Big Data saves downtime, cost of infrastructure and supports business growth. Hadoop Apache, NoSQL and Massive Analytic Databases are some tools that use Big Data architecture. Companies use Big Data to solve a customers need. The companies include IBM, General Electric, and KAGGEL.

References

Dawson, S. (2010). Seeing the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736-752. Web.

Olofson, C. (2012). The big deal about Big Data. Web.

Oracle. (2012). Oracle information architecture: An architects guide to Big Data. Web.

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