Artificial Intelligence (AI), mobile, social and Internet of Things (IoT) are driving data complexity, new forms and sources of data. Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media – much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in better and faster decisions.
Data Lake Analytics
A data lake is a shared data environment that comprises multiple repositories and capitalizes on big data technologies. It provides data to an organization for a variety of analytics processes.
A data lake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed. The term data lake is usually associated with Hadoop-oriented object storage in which an organization’s data is loaded into the Hadoop platform and then business analytics and data-mining tools are applied to the data where it resides on the Hadoop cluster.
However, data lakes can also be used effectively without incorporating Hadoop depending on the needs and goals of the organization. The term data lake is increasingly being used to describe any large data pool in which the schema and data requirements are not defined until the data is queried.
