Not the cookie you were looking for

A cookie is a file saved in the user’s system. Cookies are created when the user accesses a web page; the browser finds it easier to navigate within the site and any information the user may have given while visiting the website, such as email address. Cookies are absolutely necessary for sites having huge databases. The content of a cookie is mainly the URL of the website, the duration of the cookie’s abilities and a random number together called the ID. With such little information that the cookie contains, it is not really possible to reveal any confidential information of the user.

Session and Persistent Cookie

Without cookies, websites and their servers have no memory. Without a cookie, every time you open a new web page, the server where that page is stored will treat you like a completely new visitor. The main two categories of cookies are Session and Permanent Cookies. Web pages have no memory.  A user navigating between web pages will be treated by the website as a completely new visitor every time he visits the site. Session cookies enable the website to keep track of page visits so the user is not asked for the same information that’s already available with the site. Persistent cookie files remain in the browser’s sub folder and are activated once again once the user visits the website that created that particular cookie. Persistent cookies help websites remember the user’s information and settings when you visit them in the future. This results in faster and more convenient access. Thus, cookies allow us to proceed trough many pages of a site quickly and easily without having to authenticate or reprocess each new area of visit.

Cookie Profiling

When cookies are collected to create a certain idea about a user, it is called Cookie profiling. The information that people reveal to each site they visit can be used by system administrators to build extensive personal profiles of visitors. By automatically placing a cookie on visitor’s web browsers, servers register data on the cookie. This allows administrators to view the history of site’s users, the advertisements they have viewed and the type of online transactions they have conducted. What is important to note here is that sites can only access cookies from their own domain. While cookies can be useful in some situations, some people see this as invasion of privacy.

Ad-serving using cookies

Third-party ad serving cookies solve a lot of problems that normally arise in a situation where the website’s visitor loads content from the website but the ads come from another site. Cookies help the ad serving website. Cookies limit the number of times an ad is shown. This function comes in particularly handy when dealing with pop up ads. Cookies ensure that a pop up only shows up once per visit. Some ads are more effective when shown in a particular order or sequence. By helping the website you’re viewing remember the pages you’ve visited during your browsing session, cookies enable ads to show up in a particular order. Advertisers need to know how many times their ads were shown on publisher’s websites. Cookies allow the third party ad serving website to collect this information. Cookies allow advertisers to keep track of how many people visited the advertiser’s websites through a click or a response, on the ads shown by third party as serving companies on publisher’s websites. This feature helps both the ad serving company and the advertiser dertermine if a particular advertising campaign produced the desired results.

Drawbacks

Besides privacy concerns, cookies also have some technical drawbacks. They do not always accurately identify users and can be used for security tasks. If more than one browser is used on a computer. Each has a separate storage area for cookies. Hence, cookies do not identify a person, but  a combnation of a user account, a computer and a web browser. Thus, anyone who uses multiple accounts, computers or browsers has multiple sets of cookies.

The use of cookies may generate an incompatibility between the state of the client and the state as stored on the cookie. If the user aquires a cookie and then clicks the ‘back’ button of the browser, the state on the browser is generally not the same as before that acquisition. This can lead to confusion and bugs.

Removing Cookies

Although cookies are very useful to navigate the Internet, you definitely need to know the basics of removing your cookie files so you can protect your privacy online. There are two kinds of cookies-regular text browser cookies and flash cookies. To ensure maximum web browsing privacy, you have to delete both kinds of cookies. Too many Internet users delete cookies which are text-based and leave flash cookies intact. This doesn't protect your privacy. You have to know how to delete flash cookies, too. There is no one standardized way to remove cookies since different browsers clear cookies using different procedures. Here are the steps to disable cookies based on your browser.

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[1] http://www.allaboutcookies.org/

[2] http://www.mediabuzz.com/

[3] http://files.investis.com/

[4] http://www.londonstimes.us/

[5] https://www.youtube.com/

[6] http://www.wikipedia.org/ 

What is Data Analytics for Big Data?

There are a lot of blogs, podcasts, articles, even books about data analytics for Big Data or sometimes referred as Big Data Analytics. I wanted to write more on this subject because, Big Data or Big Data Analytics is not a buzz word that shines and disappears in a year or two. I believe Big Data or Big Data Analytics is something that we will be hearing for years to come. There is no question in my mind this will be a game changer towards data and data analytics in every field and industry. Big Data Analytics is no longer a specialized solution for cutting-edge technology companies. It is evolving into a viable, cost-effective way to store and analyze large volumes of data across almost all industries.

What is Web Analytics?

The Digital Analytics Association defines web analytics as the measure, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.[1] You can read more about Web Analytics on my previous blog Web Analytics and Data Warehouse.

What is Big Data?

Big Data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, storage, search, sharing, analysis and visualization.[2]

Some examples of Big Data include medical records, photography archives, video archives, large scale e-commerce, internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological and other complex scientific researches, web logs, RFID, military surveillance and other similar data.

Big Data technologies like Apache Hadoop, open-source software framework, provide a framework for large-scale, distributed data storage and processing across clusters of hundreds or even thousands of networked computers. The objective is to provide scalable solution for this Big Data while minimizing the processing time.

In 2010 alone, our world produced one zetabyte (1,000,000,000,000 gigabytes) of data coming from five billion mobile phones, 30 billion posts shared on Facebook per month, and millions of networked sensors connected to mobile phones, energy meters, automobiles, shipping containers, retail packaging and more [3][4]

There have been different challenges for companies to implement Big Data and Big Data Analysis projects. Some of these are

Photo Courtesy: [5]

·         For many years, companies faced upfront infrastructure cost for Big Data and Big Data Analysis projects. Also, companies were not able to respond to scale-out requirements because of infrastructure. This problem has been solved by Big Data cloud services like Amazon’s Elastic MapReduce or Microsoft’s Hadoop distribution for Windows Azure which enable companies to lease infrastructure for their Big Data projects.

Photo Courtesy: [6]

·         For most companies, integrating Big Data with other components of Data Warehouse environment is critical. Big Data does not replace Data Warehouse. Hadoop is built for fairly simple workloads, such as sorting, aggregating, converting, and filtering. It is not intended to manage schema structure and database security. Therefore, database management is still important for companies. The challenge has been how to integrate these two. IBM, Informatica, Microsoft, Oracle and SAP have released tools to interface Hadoop and relational database management systems which solved this problem.

Photo Courtesy: [7]

·         When we come to Big Data Analysis, getting user-friendly tools had been a challenge. Even though, there are some tools like Apache Pig and Apache Hive which provides SQL-like frameworks for advanced data analysts to run queries directly against data stored in Hadoop, these tools require technical expertise. Recently, Microsoft has announced the Hive ODBC driver and the Hive add-in for Excel which will allow end users to access data stored in Hadoop though Excel, Power Pivot and Analysis Services. Also, Tableau has released a tool that allow users to drag and drop Hadoop reports. These tools will allow end users to work on Big Data Analysis much more easily.

Since the above challenges have been resolved, in the coming years, we will see a dramatic growth on Big Data Analysis. Companies likely to get the most out of Big Data analytics include:[3]

Supply chain, logistics, and manufacturing	With RFID sensors, handheld scanners, and on-board GPS vehicle and shipment tracking produce vast quantities of information offering significant insight into route optimization, cost savings and operational efficiency.
Financial services	Financial markets generate immense quantities of stock market and banking transaction data that can help companies maximize trading opportunities or identify potentially fraudulent charges, among various users.
Energy and utilities	Smart instruments and electronic sensors attached to machinery, oil pipelines and equipment generate streams of incoming data that must be stored and analyzed to uncover and fix potential problems.
Media and telecommunications	Streaming media, smartphones, tablets, browsing behavior and text messages are captured at ever-increasing rates all over the world, representing a potential treasure trove of knowledge about user behavior and tastes.
Health care and life sciences	Electronic medical records systems are some of the most data-intensive systems in the world and making sense of all this data to provide patient treatment options and analyze data for clinical studies can have dramatic effect.
Retail and consumer products	Retailers can analyze vast quantities of sales transaction data to uncover patterns in users behavior and monitor brand awareness.

 References:

[1]  http://www.digitalanalyticsassociation.org/?page=standards

[2]  http://www.zdnet.com/blog/virtualization/what-is-big-data/1708

[3]  http://allthingsd.com/20120110/big-data-analytics-trends-to-watch-for-in-2012/

[4]  http://www.idc.com/

[5]  http://claritics.com/

[6]  http://www.publicpolicy.telefonica.com/blogs/blog/2012/11/09/big-data-under-analysis-at-the-oecd/

[7]  http://www.userfriendlycc.com/rates.html

[8]  http://dauofu.blogspot.com/b/post-preview?token=417-ezwBAAA.i7nDl_cKXVZdkSaA8tznjQ.dkf24Q_27en8PsXhBBRN5w&postId=5580674214298570192&type=POST

[9]  http://books.google.com/books?id=Wu_xeGdU4G8C&pg=PA3#v=onepage&q&f=false

[10]  http://mike2.openmethodology.org/wiki/Big_Data_Definition