Bigdata is a term that includes data sets which sizes beyond the ability of commonly used software tools to curate, capture, manage and process data within the tolerable elapsed time. It also describes the collection of large amount of data that would be both structured and unstructured from traditional and digital sources inside and outside. And also Bigdata explains the holistic information strategy which includes and integrates many new types of data and data management along with traditional data.
CHARACTERISTICS OF BIG DATA
Volume – Quality of generated and stored data. The data size which determines the value and potential insight to evaluate that it is big data or not.
Variety – Type and nature of data which helps people to analyze it for effective usage of resulting insight.
Velocity – The speed at which the data is generated and processed to meet the demands and challenges which lie in the growth and development path.
Variability – Data set inconsistency would hamper the process which should be able to handle and manage the data efficiency.
Veracity – The captured data would vary greatly, affecting accurate analysis.
Complexity – Data management would be a complex process, when the data can be connected, linked and correlated in order to grasp the information is referred as complexity.
IMPORTANT FEATURES FOR MANAGING BIG DATA
- High capacity, inexpensive storage.
- High performance, inexpensive processing power.
- High velocity data stream processing.
- Data integration and quality capabilities.
- Relational database acceleration.
- Unstructured sales management and search.
5 IMPORTANT TRENDS IN BIG DATA FOR 2016
Quantum Approach to Bigdata – Quantum computing is the biggest technological breakthrough since the invention of the microprocessor. This approach is to handle massive datasets could solve complex problems.
The noSQL Conques – Modeled with Bigdata deal with some non SQL tools such as apteryx, trifacata and informatica Rev
Hadoop adds to enterprise standards – Open source technology become a big part of enterprise IT landscape demand for big data analytics.
Start fishing in the Bigdata lakhs – The basic premise of this concept is how to manage, store and use the massive amounts of incoming data from a variety of mediums.
Increased Data Security and Breaches – Focus on how to handle data security before, during and after a hack.
EMERGING TECHNOLOGIES FOR BIG DATA
Column Oriented Database – Store data which focuses on columns, instead of rows and allows huge data compression.
Schema-less databases, or NoSQL databases – Focus on the storage and retrieval of large volumes of unstructured, semi-structured, or even structured data.
MapReduce – It’s a programming paradigm that allows for massive job execution scalability against thousands or clusters of servers.
Hadoop – It’s a popular implementation of MapReduce, being entirely an open source platform for handling Bigdata.
Hive – It’s a “SQL-like” bridge developed by Facebook that allows conventional BI applications to run queries against a Hadoop cluster.
PIG – It’s a “Perl-like” language developed by yahoo that allows query execution over data stored on a Hadoop cluster, instead of a “SQL-like” language.
WibiData – Combination of web analytics with Hadoop that allows web sites to work with their user data, enabling real-time responses to user behavior.
SkyTree – It’s a high-performance machine learning and data analytics platform focused specifically on handling Bigdata.