Structured Vs Unstructured Data Storage
Big Data has the potential to deliver staggering results through a significant improvement in many business functions. With massive improvements in storage, computing, and analysis capabilities, big data analytics has the potential to revolutionize how business is done today. In an age where everything changes so quickly, companies must be prepared for the next big shift in business. Today’s databases are massive and expensive; however, with a database management system that can handle the growth of a company’s data demands, businesses stand to benefit through increased productivity and profit.
Different Approaches to Big Data There are two primary approaches to handling big data, traditional data management and big data analytics. Hadoop combines both to improve data processing speed and reduce costs. The big difference between Hadoop and traditional database management systems is the storage of the data. Traditional systems have the volume of the information to be stored on the same machines as the applications themselves, and Hadoop clusters manage the volume of data per machine-or device-on a much larger scale than traditional setups.
Big Data Analytics vs. Storing Structured Data is more efficient because it reduces the amount of time that it takes to search a database for specific information. Traditional systems only allow for the search of an index over the entire system. This is because storing structured data requires a careful balance of indexes over the entire system. As such traditional systems store information in a disorganized manner, often creating a large number of small tables and a large degree of inconsistency in the system. Storing partially organized or semi-structured data using Hadoop also decreases the amount of time it takes to search a database for specific information.
Big Data Analytics Uses new technologies, Hadoop can be applied to social media to bring in even more big data from Twitter, Facebook and the other social media outlets. Applications such as these will allow users to access the same database as their data stores, thus increasing the amount of relevant and actionable information. In addition, the social media sites themselves can offer up-to-date analytics that can be integrated into Hadoop as well.
No matter what type of database is chosen, a business must ensure that the organization is able to handle the amount of processing that is required to keep it up to date. For example, a company may choose to use a fully un-structured data source with relational database management system (RDBMS), or a partially un-structured data source with a traditional relational database management system. Both types of databases require extensive memory and processing power to handle the amount of data that they contain. While a fully un-structured data source is able to meet some needs of a growing organization, a relational database management system will prove most useful when it is paired with one or more structured data storage options.
As data sets continue to grow in both volume and size, traditional relational databases will continue to lose their effectiveness. This will result in an increased amount of time spent analyzing and utilizing the available storage space for temporary data sets and will impact overall operational costs. Businesses seeking to save time while improving operational efficiency should consider investing in a fully implemented and flexible relational database management system.
