A lot of data is available these days from sources such as websites, mobile devices, documents, satellites, traffic surveillance, scientific studies, media devices, code repositories, mail servers etc. Some of the data generated from these sources are in structured form while the other is in unstructured form. Analysation and storage of structure data has been ongoing for a long time, unstructured data has appeared recently on a massive scale. Databases for storing unstructured data and analysis techniques to get results have been recently developed. Big data constitutes both structured and unstructured data. To get reliable results in the field of big data analytics, both structured and unstructured data should be combined and analyzed. We need to look at what forms both the structured and unstructured data.
Two pillars of big data analysis
1. Structured Data
Structured data refers to data that enters into a relational database (row and column oriented database structures), exists in predefined fixed fields, and is findable via search operations or algorithms. Structured data is quite simple to enter, save, find and analyze; however, it must be well-defined regarding field name and character type (e.g. alpha, numeric, date, currency, etc.). Thus, structured data is often restricted in usage because of its inflexibility. Some examples of structured data are financial details, call detail records, web server logs and human input data. Analysts and programmers working on this kind of data use structured query language (SQL) technology for relational databases (RDBMS).
2. Unstructured Data
Unstructured data does not fit into a spreadsheet or data store. However, it may have its internal structure. While unstructured data seems organized in nature, it is also treasured and increasingly available in the form of complex data formats, such as emails, text files, web pages, digital images, multimedia content, navigation details and social media posts. In fact, the majority of business interactions seem unorganized in nature. There are several ways to start assembling a database of unstructured data and processing it. Many companies have migrated to object-oriented databases like MongoDB which implement NoSQL technology for storing unstructured data. Some companies are also involved in open source big data analysis techniques, like Hadoop.
For big data analytics, analysts need to integrate structured data with unstructured data, for example, mapping customer and sales automation data to social media posts or mapping client address and audio files. No matter what the complexity and variance of structured and unstructured data are, analysts should use appropriate preparation, analysis, and visualization methods to leverage all the available data for better decision-making.
Best solution for big data analysis
However, a challenge in combining the structured and unstructured data for big data analysis is the different types of databases or systems both these types of data exist in. This has forced analytics professionals to navigate many distinct systems and move massive amounts of data, which is not desirable at all. However, the momentum is shifting towards bringing big data stores and traditional RDBMS toolsets to a single, unified data analytics platform that enables analysts to access any amount of data of any type for any analysis at any time. At Maruti Techlabs, both SQL and NoSQL technologies are being utilized for making an efficient big data analytics ecosystem. Maruti Techlabs implements logic to convert data collected from clients in RDBMS databases to NoSQL form. This new NoSQL database is analyzed by Elasticsearch, which is a tool for querying written words. Elasticsearch provides textual results that resemble a given query. Elasticsearch also offers statistical analysis of a body of text. Elasticsearch satisfies the search needs of both regular users as well as application developers.
A study of how Elasticsearch has been employed by GitHub to meet the search needs of GitHub’s users, while simultaneously providing strategic insights that help improve customer service. To solve this problem, Github used Elasticsearch and index critical event data to index its code repositories. GitHub uses Elasticsearch to index new code as soon as users push it to a repository on GitHub. This way Elasticsearch converts data from RDBMS form to NoSQL form. The data in the NoSQL form can be searched immediately after the users upload it in the RDBMS store. Elasticsearch returns search results for both public repositories, and logged-in users can see search results for any private repositories that they can access.
To conclude, good big data analytics requires joining various structured and unstructured data stores and acquiring intelligence across these data stores. While analyzing any one data store provides limited value, big data analytics working on associated structured and unstructured data stores returns exponentially more powerful insights. Businesses wishing to make the most of their data should use tools that utilize the benefits of both structured and unstructured data.
