×
Don’t stop when you’re tired. STOP when you are DONE.
--Your friends at LectureNotes
Close

BIG DATA

by Preethi Cse 005Preethi Cse 005
Type: NoteOffline Downloads: 43Views: 1398Uploaded: 7 months ago

Share it with your friends

Suggested Materials

Leave your Comments

Contributors

Preethi Cse 005
Preethi Cse 005
UNIT I INTRODUCTION TO BIG DATA Introduction to Big Data Platform: Big data has become viable as cost-effective approaches have emerged to tame the volume, velocity and variability of massive data. Within this data lie valuable patterns and information, previously hidden because of the amount of work required to extract them. To leading corporations, such as Walmart or Google, this power has been in reach for some time, but at fantastic cost. Today’s commodity hardware, cloud architectures and open source software bring big data processing into the reach of the less well-resourced. Big data processing is eminently feasible for even the small garage start-ups, who can cheaply rent server time in the cloud. The value of big data to an organization falls into two categories: 1. Analytical use 2. Enabling new products. Big data analytics can reveal insights hidden previously by data too costly to process, such as peer influence among customers, revealed by analysing shoppers’ transactions, social and geographical data. Being able to process every item of data in reasonable time removes the troublesome need for sampling and promotes an investigative approach to data, in contrast to the somewhat static nature of running predetermined reports. Google has introduced Map Reduce framework for processing large amounts of data on commodity hardware. Apache’s Hadoop distributed file system (HDFS) is evolving as a superior software component for cloud computing combined along with integrated parts such as Map Reduce. Hadoop, which is an open-source implementation of Google Map Reduce, including a distributed file system, provides to the application programmer the abstraction of the map and the reduce. With Hadoop it is easier for organizations to get a grip on the large volumes of data being generated each day, but at the same time can also create problems related to security, data access, monitoring, high availability and business continuity. Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed. The totality of data related to patient healthcare and well-being make up “big data” in the healthcare industry. Big data analytics is the process of examining large data sets containing a variety of 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.
Big Data Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or does not fit the structures of traditional database architectures. In other words, Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. To gain value from this data, you must choose an alternative way to process it. Big Data is the next generation of data warehousing and business analytics and is poised to deliver top line revenues cost efficiently for enterprises. Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data. Definition Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, create, manage, and process the data within a tolerable elapsed time Big data is high-volume, high-velocity and high-variety information assets that demand costeffective, innovative forms of information processing for enhanced insight and decisionmaking. Big data is often boiled down to a few varieties including social data, machine data, and transactional data. Social media data is providing remarkable insights to companies on consumer behavior and sentiment that can be integrated with CRM data for analysis, with 230 million tweets posted on Twitter per day, 2.7 billion Likes and comments added to Facebook every day, and 60 hours of video uploaded to YouTube every minute (this is what we mean by velocity of data). Machine data consists of information generated from industrial equipment, real-time data from sensors that track parts and monitor machinery (often also called the Internet of Things), and even web logs that track user behavior online. At arcplan client CERN, the largest particle physics research center in the world, the Large Hadron Collider (LHC) generates 40 terabytes of data every second during experiments. Regarding transactional data, large retailers and even B2B companies can generate multitudes of data on a regular basis considering that their transactions consist of one or many items, product IDs, prices, payment information, manufacturer and distributor data, and much more. Major retailers like Amazon.com, which posted $10B in sales in Q3 2011, and restaurants like US pizza chain Domino's, which serves over 1 million customers per day, are generating petabytes of transactional big data. The thing to note is that big data can resemble traditional structured data or unstructured, high frequency information.
Big Data Analytics Big (and small) Data analytics is the process of examining data—typically of a variety of sources, types, volumes and / or complexities—to uncover hidden patterns, unknown correlations, and other useful information. The intent is to find business insights that were not previously possible or were missed, so that better decisions can be made. Big Data analytics uses a wide variety of advanced analytics to provide 1. Deeper insights. Rather than looking at segments, classifications, regions, groups, or other summary levels you ’ll have insights into all the individuals, all the products, all the parts, all the events, all the transactions, etc. 2. Broader insights. The world is complex. Operating a business in a global, connected economy is very complex given constantly evolving and changing conditions. As humans, we simplify conditions so we can process events and understand what is happening. But our best-laid plans often go astray because of the estimating or approximating. Big Data analytics takes into account all the data, including new data sources, to understand the complex, evolving, and interrelated conditions to produce more accurate insights.
3. Frictionless actions. Increased reliability and accuracy that will allow the deeper and broader insights to be automated into systematic actions. Advanced Big data analytics Big data analytic applications

Lecture Notes