BIG DATA ANALYTICS

Defining Big Data

Big data analytics is the process of examining large data sets containing a variety of data types -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.


The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs. That could include Web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things. Some people exclusively associate big data with semi-structured and unstructured data of that sort, but consulting firms like Gartner Inc. and Forrester Research Inc. also consider transactions and other structured data to be valid components of big data analytics applications.



Technology that makes it possible

Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, data mining, text analytics and statistical analysis. Mainstream BI software and data visualization tools can also play a role in the analysis process. But the semi-structured and unstructured data may not fit well in traditional data warehouses based on relational databases. Furthermore, data warehouses may not be able to handle the processing demands posed by sets of big data that need to be updated frequently or even continually -- for example, real-time data on the performance of mobile applications or of oil and gas pipelines.


As a result, many organizations looking to collect, process and analyze big data have turned to a newer class of technologies that includes Hadoop and related tools such as YARN, MapReduce, Spark, Hive and Pig as well as NoSQL databases. Those technologies form the core of an open source software framework that supports the processing of large and diverse data sets across clustered systems.


Big Data Analytical Products

Big Data Analytics has been dominated by Apache Foundation. With the in-house Spark Streaming integrated with Apache Spark this opens up a whole new dimension in the mid-stream analytics segment. In essence, the analytics based on HDFS still remain essentially the same as the Fast Data platform, since Fast Data often complements processing of data stored in the Big Data platforms (such as HDFS). However, Big Data Analytics do not necessarily depend on streaming to realize analytics. Its hard to imagine, but Map-Reduce programming model (batch-oriented) is already beginning to wane (even before it gained recognition) in presence of new stream-based platforms.

In-Memory Computing


TIBCO ActiveSpaces

Apache Spark (Framework)

Data Streaming


TIBCO StreamBase

Spark Streaming (Framework)

Visualization Analytics


TIBCO Spotfire

TIBCO JasperSoft

Apache Framework (Zeppelin)