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Big Data Applications in Online Retail

This is the first of a series of posts where I simply list some applications of big data analytics in various industries and related business opportunities. In retail, especially the online retail market, the business growth and profitability has direct connection to customer. Marketing campaigns are...

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What is Machine Learning?

Posted by Anahita | Posted in Business Intelligence, Technology | Posted on 03-02-2013

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Computers and statisticians both can use data, but the way the process is done is completely different. Statistics is about the use of data to enable humans to conclude patterns and gain insight from the data. On the other hand statistical and mathematical models and methods can be applied to produce tools and methodologies for computers. These then are used by the machine to perform the required tasks.

When we teach the computers to give us insight about the data, we teach them to extract information from the data through algorithms in order to identify the patterns from a mass volume of noise. These algorithms, also known as Patten Recognition Algorithms, are also used to automate required tasks, enable us to train the machines to put data into certain contexts using a training set of data.

There are two main types of problems that are solved through the machine learning: classification and regression.

In future posts I will introduce you to some of the methods used in machine learning and their real life applications in Big Data.

Windows Azure 90-Day Free Trial

Posted by Anahita | Posted in Business Intelligence, Technology | Posted on 26-12-2012

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You can get a 90 day free trial of Windows Azure. That will give you 750 HRS of Cloud Services: 750 small compute hours, 35 GB Storage with 50M transactions, 1 DU SQL Database with 1 DU of Web Business Edition, and 20 GB Data Transfers, Outbound and unlimited inbound, 10 Web Sites and Mobile Services Stays free after the 90-day  trial.

Hadoop Explained!

Posted by Anahita | Posted in Business Analytics, Technology | Posted on 09-12-2012

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Big Data Explained!

Posted by Anahita | Posted in Business Intelligence, Technology | Posted on 09-12-2012

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Big Data Solution: IBM InfoSphere BigInsights

Posted by Anahita | Posted in Technology | Posted on 20-01-2012

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Big Data again, as this subject fascinates me. Looking into tools and technologies, I already posted about open source Apache Hadoop projects, HDFS and MapReduce.

 

IBM offers two editions of its InfoSphere BigInsights which are compatible with Apache Hadoop ecosystem for handling the big data.

The Basic Edition of InfoSphere BigInsight is a free download edition that includes a fully integrated and compatible version of Apache Hadoop  and related components. This comes with a web based management console and the ability to integrate with IBM InfoSphere Warehouse, IBM Smart Analytics System and finally DB2 for Windows, Unix and Linux. Complete with Jaql, a SQL like query language for both structured and non-traditional data types.

The Enterprise Edition supports structured, semi-structured and non-structured data, and massive data scale out, while running on commonly available hardware. An enterprise class management including job management  and security features including Active Director/LDAP authentication.

More details on editions and pricing is available from the IBM website.

 

 

 

 

 

Big EDW!

Posted by Anahita | Posted in Agile, Business Intelligence, Data Warehouse, Technology | Posted on 09-01-2012

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Big Data is changing the way we need to look at Enterprise Data Warehousing. Previously I posted about big data  in Big Data – Volume, Variety and Velocity!. I also posted about the supporting projects from Apache Hadoop, such as Hbase and Hive in Big Data, Hadoop and Business Intelligence. Today I want to introduce a new concept, or better say an original idea. Big EDW!  Yes, Business Intelligence and Data Warehousing also will have to turn to Big BI and Big EDW!

So what makes the fabric of Big EDW and Big BI Analytics? The answer is the ability to analyse and make sense of Big Data, which covers not only the 20% of the structured data that organisations keep on their relational and dimensional databases, but also the vast remaining 80% unstructured data scattered in digital and web documents such as Microsoft Word, MS Excel, MS PowerPoint, MS Visio,  MS Project, as well as web data such as social media, wikis, web sites and other formats such as pictures, videos, and log files. I have posted about the meaning of unstructured data  previously  in On Unstructured Data.

Traditionally Enterprise Data Warehouse is a centralised Business Intelligence System, containing the required ETL programs to access various data sources,   transformation and load into a well designed dimensional model.  The front end BI access tools such as reporting, analytical and dashboards then is used on their own or integrated with the organisations interanet, to give the right users timely access to relevant information for analysis and decision making activities.

The Big Data does not quite  fit into this model for three main reasons, volume, variety and velocity of change and growth. Big EDW will need to break some of the traditional data warehousing concepts, but once done, it will create value that has many folds of magnitude.

Big EDW, should have the ability to be quick and agile in dealing with Big Data. It has to make it available for quick access to many new available data sources  in high volume. Enhanced design patterns or new use cases  have to emerge to make this possible. These patterns and use cases  should make use of more intelligent and faster methods of providing the relevant data when  required. This could be achieved by many methods such as  dimensional modelling, advanced mathematical/statistical models such as bootstrap and jackknife sampling to provide more accurate results for more accurate approximation for mean. median, variances, percentiles and standard deviation of big data.   Apache Hadoop  plays an essential role with projects such as  MapReduce, HDFS, HSQL (Hive SQL) and HBase. New central monitoring tools should be developed and embedded within the Big EDW to handle big data metadata such as social media sources, text analysis, sensor analysis, search ranking, etc.  Parallel Machine Learning and Data Mining, being looked at recently via projects such as Apache Mahout and Hadoop-ML combined with Complex Event Processing (CEP), amongst faster SDLC and project methodologies such as agile scrum for handling the Big EDW life cycle are also becoming standard in the realm of Big EDW.

Note that the phrase “Big EDW”  is not used anywhere else and is the naming that I thought could fit EDW growth in to a system that can also accommodate and manage  Big Data!

 

 

 

 

 

 

 

Protected: SQL Server 2008 Editions

Posted by Anahita | Posted in Technology | Posted on 18-12-2011

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