Common business solutions and conventional analytics are now giving way to more informed and complicated Big Data analytics. With better analyzed results in their hold, data scientists are making more result-oriented data-driven decisions, analyzing massive volumes of data, and amassing more useful knowledge databases than ever before. So, how do billions of rows of structured, semi-structured and unstructured data, with millions of combinations in abundant formats and multiple data stores, gets analyzed?
Read on for how high-performance analytics and other processes are being used for determining the relevance of Big Data deluges.
Innovation Driven Techniques for Big Data Analysis
With paradigm shifts in computing technology being the order of the day, high-performance data mining, text mining, predictive analytics, forecasting, and tools for optimizing of Big Data are continuously driving innovation and perfecting the base for tackling challenging business decisions. This in turn is leading to faster and lesser complicated processing of extremely large-sized databases. These analytics are also addressing fast-paced Big Data requirements in more ways than one. For instance, Amazon, Google and other Big Data driven companies are mastering this art of analyzing, and thus gaining their much needed competitive advantages.
Using Software Tools for Big Data Analytics
Users of advanced analytics disciplines like text analytics, statistical analysis or data mining are aware of the software tools and techniques that are useful for Big Data analytics too. Mainstream data visualization and BI software tools are now playing a significant role in these analysis processes. This is specifically true for semi-structured/unstructured data that’s incapable of fitting into conventional relational databases, or those that require continuous updating for higher relevance—such as real-time data depicting the performance of oil/gas pipelines or mobile applications.
Under the circumstances, organizations are now collating, processing and analyzing Big Data with an altogether new category of technology that encompasses Hadoop, NoSQL databases, and related tools such as MapReduce, Spark, YARN, Hive and Pig. These technological inclusions are at the core of open source software frameworks supporting diverse and mammoth-sized data, across clustered systems (also consider checking out this perfect parcel of information for data science degree).
Data Analysis via NoSQL and Hadoop
In certain situations, NoSQL systems and Hadoop clusters are also being used as staging areas and landing pads for Big Data before its loaded into warehouses for further analysis (more often than not, in summarized forms conducive to the purpose of relational structures). This is the reason why Big Data vendors are recommending that Hadoop data lakes should serve as the primary repository for all incoming streams of organizational unstructured data. Such architectures allow data subsets to be filtered for better usage in analytical databases and data warehouses. Big Data can also be analyzed in Hadoop directly, by using stream processing software, batch query tools and ad hoc SQL queries on Hadoop.
Big Data Analysis: Levels and Descriptions
The various levels of Big Data analysis are:
Why do you Need Big Data Analytics?
With the abilities of analyzing Big Data, you open the doors of plentiful opportunities for your organization (also consider checking out this career guide for data science jobs). From mere sampling of large-sized data sets through conventional means, you can now use advanced tools for more detailed and complete raw data analysis. For instance, with the right analytics in place, you can use Big Data for predicting customer behaviour, analyzing driving patterns for more accurate insurance premium estimations, including analytics-driven strategy for better bottom line figures, and so forth. So, gear up to analyze your warehouse with all kinds of data—unstructured, semi-structured, and well-structured alike.
Your analytical findings are capable of leading you to better customer services, more effective marketing tactics, improved operational efficiency, newer revenue opportunities, and targeted competitive advantages over others.
All the best!