Top 10 Big Data Technologies Of Present Times

Over last few years Big Data technologies are getting due attention and there are several trends and innovations in this space in recent times.    Wednesday, October 22, 2014:  Big Data is a concept which is quite broad and comprises several trends and technology developments. Over last few years Big Data technologies are getting due attention and there are several trends and innovations in this space in recent times. Here we'll discuss top ten emerging Big Data technologies. 1. Column-oriented databases:  Traditional databases are excellent in online transaction processing but when it comes to query performance while data volumes grow, these databases fall short on performance. The new column-oriented databases store data and focuses on columns and not rows. It allows huge data compression and faster query times. 2. Streaming Big Data analytics:  There are several projects in this section including Storm, Spark, Data Torrent, Spring XD and SQL Stream. Apache Storm is an open source distributed real-time computation system which simplifies streams of data and real-time processing. Spark is a data processing platform which is compatible with Hadoop. DataTorrent is a real-time streaming platform which enables businesses to perform data processing. Spring XD supports streams for event driven data while SQLStream provides a distributed stream processing platform for streaming analytics, visualization and continuous integration of machine data. 3. Schema-less databases, or NoSQL databases:  This database category includes key-value stores and document stores. This database focuses on storage and retrieval of large volumes of unstructured, semi-structured or even structured data. 4. SQL-in-Hadoop:  This technology includes Apache Hive, Shark, Apache Drill, Presto and Phoenix among many others. It helps in making queries and it also manages large datasets in distributed storage. Shark is a data warehouse system which supports Hive's query language. Apache Drill is an Apache incubation project and it's designed for scalability. It's backed by MapR. Presto is an open source distributed SQL query engine and Phoenix is an open source SQL query engine for Apache Hbase. 5. MapReduce:  It's a programming paradigm which allows massive job execution scalability against thousands of servers or clusters of servers. Its two tasks are Map task and Reduce task. It converts any input dataset into different set of value pairs while reducing set of tuples. 6. Hadoop:  Hadoop is an open source platform for handling Big Data which can work with multiple data sources. It has other applications too and it's largely used for changing data like location-based data from weather or traffic sensors, web-based or social media data or machine-to-machine transactional data. 7. PIG:  PIG brings the Hadoop project close to developers and business users and it's used by Perl like language allowing query execution over data stored on a Hadoop cluster. PIG was a project by Yahoo! But now it's completely open source. 8. Big Data Lambda Architecture:  Lambda Architecture is a hybrid platform which combines real-time data and pre-computed data to provide a near-real time view of the data at all times. Its frameworks include Summingbird by Twitter and Lambdoop. 9. PLATFORA:  It almost copies Hadoop and it requires developer knowledge to operate. It's a platform which turns queries into Hadoop jobs with immediate effect and creates an abstraction layer to simplify the datasets in Hadoop. 10. SkyTree:  It's a high performance machine learning data analytics platform which handles Big Data. It's an essential part of Big Data. Courtesy: TechRepublic and InfoQ  Sanchari Banerjee, EFYTIMES News Network

Big Data and Data Science Books - A Baker's Dozen

• Posted by Kirk Borne on January 15, 2014 at 12:30pm
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Here are 13 informative and inspirational books on Big Data and Data Science.  This is definitely not intended to be a comprehensive list (since a complete list of such readings would itself be a form of "Big Data", and consequently the number of possibilities is a nearly uncountable number!NOTE definition of "uncountable" = an infinite set that contains too many elements to be countable.)

1. Big Data: A Revolution That Will Transform How We Live, Work, and T..., by Viktor Mayer-Schonberger and Kenneth Cukier
2. The Signal and the Noise: Why So Many Predictions Fail-but Some Don't, by Nate Silver
3. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie..., by Eric Siegel 
4. The Human Face of Big Data, by Rick Smolan and Jennifer Erwitt
5. Data Science for Business: What you need to know about data mining ..., by Foster Provost and Tom Fawcett
6. The Black Swan: The Impact of the Highly Improbable, by Nassim Nicholas Taleb
7. Competing on Analytics: The New Science of Winning, by Thomas H. Davenport and Jeanne G. Harris
8. Super Crunchers: Why Thinking-by-Numbers is the New Way to Be Smart, by Ian Ayres
9. Big Data Marketing: Engage Your Customers More Effectively and Driv..., by Lisa Arthur
10. Journeys to Data Mining: Experiences from 15 Renowned Researchers, by Mohamed Medhat Gaber (editor)
11. The Fourth Paradigm: Data-Intensive Scientific Discovery, by T.Hey, S.Tansley, and K.Tolle (editors)
12. Seven Databases in Seven Weeks: A Guide to Modern Databases and the..., by Eric Redmond and Jim Wilson
13. Data Mining And Predictive Analysis: Intelligence Gathering And Cri..., by Colleen McCue

And here are two more, as a bonus:

 14. A Statistical Guide for the Ethically Perplexed, by Lawrence Hubert and Howard Wainer

 15. Too Big To Ignore: The Business Case for Big Data, by Phil Simon 

The 3 reasons why Big Data benefits your business

Big Data, Simple Guidance

by Microsoft

MSFT for Work 

6 Jan 2014 12:49 PM 

• 0

Big data is very simple—it’s an extremely large set of data—but the confusing part is knowing what to do with all of that information. Big data can either be a tremendous asset to your business or a time-sapping mire.David McJannet, VP of marketing for Microsoft partner Hortonworks, provides a simplified definition to shed some light on what big data is and why it’s important:
Big data is about building new analytic applications based on new types of data, to better serve your customers and drive a better competitive advantage.
There are three key reasons data insights are important to your business:

1. You can harvest intelligent data by using data solutions that comb the entire web and gather relevant information to create actionable analytics. Getting the right data is the first step to actionable insights.
2. Empower employees with real-time insights by delivering data visualization to anyone in your organization. Data will be much more helpful if your employees can use it easily.
3. Create a connected, data-driven organization that eliminates silos by connecting people through shared processes and data. Open the floodgates and give everyone access to data, and the insights driven by that data.

Two departments within most organizations that will be affected by more effective business insights are Marketing and Finance:

1. Reimagine Marketing driven by deep customer insights to create engaging, personal advertising. Marketing can be more personal than ever before—one of the best ways for it to work effectively.
2. Reimagine Finance by translating data into business impact. Assess and control risk, find ways to reduce cost, and strategize growth and competition. Predictive models will enable your business to anticipate changes most businesses were guessing at 10 years ago.

Big data is about driving actionable insights for your business. The data-driven organization will be able to better understand their customers and business challenges to provide a competitive advantage.