The Predicament of Extracting Big Data Patterns
Like any other person out there in the world that creates data or has something to do with data understands the predicament of deciphering data in general to gather a meaning insight of things from it. But as we all know data is tactfully divided into two genres—structured data that is organized in a format or repository, and unstructured data that comprises of data without format such as emails, as well as social media generated data. Data scientists spend most of their time cleaning these data to gain a market or consumer pattern that would help companies steer themselves towards more profitability.
Formatting and merging the unstructured data with the structured data is a tedious process, but on the other hand it’s a crucial one as well. Data scientists look forward to easier processes that would help them efficiently organize the unstructured data and in turn upload the relevant data within the right placement of the structured data format. The integration of structured and unstructured data is necessary as most of the industry draw market patterns from the uncategorized unstructured data. Nevertheless, the challenge of obtaining a measurable and accurate form of big data often surpasses the human capacity to produce intricate error free information. Structured data integration into unstructured data is difficult to maintain as the metadata created often has the problem of obtaining accurate factual data. The unstructured data are often hard to analyze using the conventional form of business intelligence analysis tool as it is basically produced to process structured data.
The Big data obtained gives shape to the market campaigning and brand imaging as well as influences the future customer metrics. Therefore, web logging has become one of the highly scalable methods of merging structured data format with that of the uncategorized mass metrics and information. Database management system like Apache Cassandra, Microsoft SQL Server, Microsoft Access, Oracle RDBMS, IBM DB2, Teradata are some of the major contenders in providing enterprise level data management of unstructured data, NoSQL as well as web logging solutions. Open Source Big Data tools are an important source of reviewing and processing Big Data analytics by merging the factual information obtained from unstructured data and incorporating them into the structured data as one format. Apache Hadoop, Apache storm, Lumify, Apache Samoa, HPCC Systems Big Data, Talend Open Studio for Big Data, and Elasticsearch are some of the important Open Source Big Data tool available in the market.
Content Management System (CMS) of various types has been produced for businesses and enterprises over the years to manage the rapidly growing mass of unstructured data within the cloud—hybrid, public, private—system. Unstructured data are mostly part of the containers such as .doc, .ppt, tiff, .html format, therefore XML, a markup language, is used to encode the data in the containers into a specific format. The XML data are further formatted into semantic metadata model that extracts proper meaning and patterns from the arrays of scattered data. The data obtained is then leveraged to facilitate the searching of a meaningful and required data.
Data production from innumerable sources are increasing in size rapidly and taking up more storage space. Hence, there arises the need of combining the structured and unstructured data and removing the unnecessary data. Even though big data has become one of the most important sources of marketing, but giving them potential energy production will eventually leave a harmful impact on global climate.