Are You Ready for Big Data?
By Roopal Bhatia | Submitted On February 10, 2011
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There is a lot of buzz around Big Data and the NOSQL movement these days and rightly so. The issues with data have essentially been two-fold: find cost effective ways to store ever increasing amounts of data and information, and find ways to mine this information to extract meaningful Business Intelligence.
This problem has been
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Try performing a join between two database instances and you will know what I am talking about. To solve these issues, there are custom solutions from vendors like Teradata and Netezza. The barrier for entry is still quite high in adopting these systems, however, both in terms of license fees and setup and maintenance costs.
There is an alternative. We are now in the era of framework-based DW, DIY DW and DW in the Cloud. The current set of tools and technologies that have emerged have helped democratize this domain which was for long the exclusive preserve of a few select vendors. The revolution was led by grid-based implementations adopted by the leading players like Google (Bigtable), Facebook (Cassandra) and Yahoo (Hadoop).
Hadoop has emerged as one of the most popular Map/Reduce based open source frameworks for Big Data and several Information majors have adopted this technology. Beware that this is a framework and may need significant amounts of customization and programming to get it to do what you want. If Hadoop is not your cup of tea, then there are similar implementations like AsterData and GreenPlum which work on the same concepts but can get you up and running very quickly with their own abstractions libraries like SQL-MR and intelligent dashboards for easy configuration and maintenance. Another very appealing feature of these offerings is their ability to be hosted in a Cloud so all your
There are many vendors that are using Hadoop technology to improve their business but in this paper I will be talking about only three of them, such as Amazon Web Services Inc (AWS), Pivotal Corp and Datastax Inc. AWS own and maintain the network-connected hardware required for these application services, while you provision and use what you need via a web application. AWS is a provider of cloud computing, which refers to the on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. Pivotal Corp offers a modern approach to technology that organizations need to thrive in a new era of business innovation. Their solutions intersect cloud, big data and agile development, creating a framework that
Businesses using data is not a new concept; however, the role of data within industries has increased dramatically over the years to the point that it is essential for a business to understand how to handle data in order to continue operations. In today’s bustling digital age, professionals credit a certain type of data called “big data” with helping businesses gain insight on consumers. Big data is created whenever you travel to your favorite restaurant, make a particular move in a video game, swipe your card to purchase your favorite pair of Crocs, or tell your Facebook friends what you had for breakfast. It is data that is too large to be captured and processed by standard business
As Big Data grew and environments supporting Big Data become more robust, the data being stored by businesses evolved in complexity as well. All manner of nonstandard text (music, images, freeform text, videos) began being captured in the Big Data ecosystem. The changing needs of the data environment resulted in the creation of NoSQL databases. These databases (built on work done at Google and at Amazon) were optimized to store and retrieve data modeled using non-traditional non-tabular entities. And of course, Big Data cannot exist in a vacuum – it requires tools to process, analyze, and display the vast amounts of information in a manner comprehensible to mere humans. This has led to the rise of Big Data BI.
As a result of the appearance of big data in our world, conventional data warehousing and data analysis methods no longer have the process power needed. What is Big Data you may ask and why is it such a big deal. NIST defines big data as anywhere “[…] data volume, acquisition velocity, or data representation limits the ability to perform effective analysis using traditional relational approaches […]” (Mell & Cooper, n.d.).
the ‘big Data era’ has arrived — multi-petabyte data warehouses, social media interactions, real-time sensory data feeds, geospatial information and other new data sources are presenting organisations with a range of challenges, but also significant opportunities. IDC believes that as CIOs start to adopt the new class of technologies required to process, discover and analyse these massive data sets that cannot be dealt with using traditional databases
Hadoop excels with managing and processing file-based data, especially when the data is voluminous in the extreme and the data would not benefit from transformation and loading into a DBMS. In fact, for the kinds of discovery analytics involved with Hadoop, it’s best to keep the data in its raw, source form. This is why Hadoop has such a well-deserved reputation with big data analytics.
Hadoop is one of the most popular technologies for handling Big Data as it is entirely open source. One of the reasons why Hadoop is used is because it is flexible enough to be able to work with multiple data sources. The multiple data sources can be combined in order to enlarge scaling processing and it can run processor intensive machine learning jobs through reading data from a database says Rodrigues in his article on Big Data. He states that Hadoop has many different applications but one that it excels in is being able to handle large volumes of data that are constantly changing.This is extremely good as it receives location based data from traffic devices and weather satellites. They also work with social media data and web-based data as
RDBMS are intolerable for large data volumes. NoSQL distributed databases, allow data to be spread across thousands of servers and can store large volume of data.
Apache Hadoop is an open source framework and its helps in the distributed processing of
Hadoop is an Apache open source software (java framework). It runs on cluster of commodity machines and provides both distributed storage and distributed processing of huge data sets. It is capable of processing data sizes ranging from Gigabytes to Petabytes.
Big Data is changing the way business decisions are made. It is no longer a passing fad and if companies are not extracting insights from the data collected, they will be left behind by their competition.
ig data is a large amount of data, structured as well as unstructured, that records or entails information about business on a daily basis (“Big data: What,” n.d.). This huge amount of data contains information about different aspects but not all this information is important to us, therefore, mining for information through such a large amount of data is a very important step. Most of the time, the magnanimity of such data is so much that it is difficult to process it using conventional database and software programs (Beal, 2016). In this age of modernization, the fame and popularity of the term “Big Data” is constantly increasing. Owing to its renown and eminence, Oxford English Dictionary added it in 2013 and it also appeared in Merriam-Webster’s Collegiate Dictionary (Dutcher, 2014).
Hadoop is the current pinnacle of big data technology. It is cheap, highly customizable, and incredibly effective. My firm, an international oil tool rental company, is in a perfect position to implement a Hadoop system. I believe that Cloudera, Hortonworks, and IBM are the top vendors for my company. The power of Hadoop is immense, with a future that promises even more innovation and capability.
There is a wide range of paid or open source tools and techniques for big data analytics: statistical analysis, online analytical processing (OLAP) tools \cite{dwh}, data warehouses (DWH) \cite{dwh}, distributed programming models (e.g., MapReduce \cite{mapreduce}), clouds \cite{cloudcomputing}, complex event processing \cite{cep}, etc. \cite{russom}.
Management of big data is useful with how it is used. Ways to use the stored information include, but not limited to, reduction of costs, time reductions, and making smart decisions based on data results. [1]