The term Big data that describes any voluminous amount of structured, semi-structured and unstructured data, that has the potential to be mined for information.
Big data, characterized by 3V’s
- Volume, extreme volume of data
- Variety, excessively wide variety of types of data
- Velocity, the speed at which the data gets processed.
Although big data does not refer to any definitive quantity, the term is often used when clear-cut petabytes and exabytes of data, such type of data cannot be integrated easily.
Big Data transforms to 4V’s
The fourth ‘V’ that is important for the organization is the ‘Veracity’ which is performed to handle the uncertainty. Huge data in high speed gets stored and the identifying of the incorrect data specially in the automated system is a challenge to make sure that both the data and the analysis are correct.
Because Big Data takes redundant time and costs replete money to load into a traditional relational database for analysis, new access to storing and analyzing data have appeased that rely less on data schema and data quality. Preferably, raw data with extended metadata is aggregated in a data pool, machine learning and artificial intelligence programs use complicated algorithms to look for recursive patterns.
Big data analytics is often blend with cloud computing since the analysis of large data sets in real time requires a platform like Hadoop to store huge data sets in the distributed cloud, MapReduce to parallel , combine and process data from multiple sources.
The demand for big data analytics is big giant, there is currently a lack of data scientists and other analysts have practice working with big data in a distributed and open source environment. In the venture, vendors have responded to this lack by creating Hadoop appliances to avail corporate to take advantage of the semi-structured and unstructured data they mine.
Big data can vary with small data, another emerging term that is often used to characterize data with volume and format that can be easily used for own service. A commonly quoted dictum is that “Big Data is for Machines, Small Data is for People.”
Traditional, row-oriented databases are admirable for online transaction large data with high update speeds, but they fall short on query performance as the data volumes excessively grow and as data becomes more unstructured. Column-oriented databases store data on columns, not of of rows, allowing for large data compression and very fast query times. The downside to these databases is that they normally allow only batch updates, having a very slower update time than traditional models.
NoSQL Databases or Schema-less Databases
There are variety of database types that fall into this category, like as key-value stores and document stores, that focus on the storage and accrue of large amount of unstructured, semi-structured, or even structured data. They accomplish performance gains by doing away with some or all the limitations traditionally associated with conventional databases, like as read write consistency, in change for scalability and distributed processing.
It is a programming model that allows for massive data execution scalability against thousands of servers or clusters of servers. Any MapReduce implementation contains two tasks:
- The “Map” task, that an input dataset is converted into a different set of key/value pairs, or tuples.
- The “Reduce” task, where several of the outputs of the “Map” task are joined to form a reduced set of tuples.
Explore the Tools
Here are the set of open source tools to support the Big Data implementation. A brief overview gives creates interest to move further.
Hadoop is widely used for implementation of MapReduce, being a total open source environment for handling Big Data.
It is flexible tool that has the ability to work with multiple data sources, either aggregating multiple sources to do large scale processing, or even reading data from a database in order to run processor intensive machine learning jobs. It has several different applications, but one of the top use cases is for big volumes of continuous changing data, for example location-based data from weather report or traffic sensors, web-based or social media data, or machine-to-machine transaction data.
Hive is a SQL-like integration tool that allows conventional BI applications to run queries against a Hadoop cluster.
It was originally framed and developed by Facebook, but has been made open source, and it’s a higher order level abstraction of the Hadoop framework that can allows any user to make queries against data stored in a Hadoop cloud cluster just as if they were manipulating a conventional data storage. It amplifies the reach of Hadoop, making it more convenient for BI users.
PIG is another integration tool that tries to bring Hadoop closer to the realities of developers and users, like to Hive.
Unlike Hive, however, PIG consists of a “Perl-like” language that allows user for query execution over data stored on a Hadoop cluster cloud, instead of a “SQL” language. PIG was developed by Yahoo!, and, just like Hive, has also made available in open source.
WibiData is an integration of web analytics with Hadoop, that built on top of HBase, which is itself a database layer on top of Hadoop. It allows websites to better performance and work with their user large data, enabling real-time responses to user’s behavior, like as serving personalized content, decisions and recommendations.
PLATFORA the greatest limitation of Hadoop, it is a very low-level implementation of MapReduce, requiring huge developer knowledge to operate.
A full cycle can take hours between preparing, testing and running jobs, eliminating the interactivity that users enjoyed with conventional databases. PLATFORA is an open source platform that turns user’s queries into Hadoop jobs automatically, that creating an abstraction layer that any user can exploit to organize and simplify the datasets stored in Hadoop.
SkyTree is a high-performance machine learning and data analytics platform mainly focus on handling large volume of data (Big Data).
Machine learning is an essential part of Big Data Storage since the huge data volumes make manual exploration, or even conventional automated analysis methods un achievable or too expensive.
Storage Technologies As the data volumes increase, we need for effective and efficient storage techniques. The main evolutions in this are related to data compression and storage virtualization.
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