The most ambitious project of India, Aadhaar project relies completely on Big Data. From collection of data to storage and utilization of biometric information of the entire population, big data till date has crossed over the billion mark. It is needless to say, a project of such a vast magnitude must be plagued with ample challenges but as its powered by big data, the chances of success is high.
Basically, Aadhaar is a unique 12 digit number assigned by the UIDA, Unique Identification Authority of India to an individual residing in India. The project was launched in the year 2009 under the supreme guidance of former Infosys CEO and co-founder Nandan Nilekani. He was the sole architect of this grand project, which required several added inputs from various other sources.
MapR, a business software company headquartered in California is providing technology support for the most-ambitious Aadhaar project. It is the developer-cum-distributer of “Apache APA +0.00% Hadoop” and for quite some time it is optimizing its well-integrated web-scale enterprise storage and real-time database tech for this project.
The encompassing technology architecture behind Aadhaar is structured on the principles of openness, strong security, linear scalability and vendor neutrality. The system is expected to expand with every new enrollment, which means it’s required to handle millions of transactions through billions of records, each day.
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The landscape of employment is changing. The job opportunities in the field of data science is surging onwards, that too at a robust rate. Continue reading →
Are you too scared to even think that machines will fly away with your job?
Do you support that the rise of AI will make humans obsolete?
Nowadays, AI can perform a large number of tasks, right from managing insurance claims to handling investment portfolios to solving HR related stuffs. Amidst all, do humans stand a chance to fight against the Automation Apocalypse? If yes, then how?
As per 2016 reports, McKinsey analyzed 830 occupations and concluded that only 5% of them could be automated. Amazon also showed somewhat similar picture. Within 3 years, the number of robots operated by the company got increased from 1400 to 45000, which is quite a number, but the number of employees hired remained unchanged.
While Automation Apocalypse is raging a war in the tech world, majority of techies don’t feel the urgency of being scared for jobs. They don’t have any problem in learning new stuffs, which would eventually make them more tech-savvy.
AI will eventually make workforce of any organization more powerful. Machines make their jobs easier and that definitely work in favor of the company in question.
When computers were invented, did it eat away your jobs? No, so this time too, nothing like that is going to happen. Even, numerous surveys speak in favor of automation.
The post depression bedlam is clearing for developed nations. Nevertheless, the household debts are shooting up, making credit risk managers face growing default rates. As per the reports of International Finance, household debts have risen by USD 7.7 trillion since the year 2007 till 2015. At present, the debts stand at a whopping amount of USD 44 trillion – the figures can give anyone a nightmare!
In such a topsy-turvy situation, credit risk managers should look for ingenious methods to lower default rates and keep accuracy in check. Application of data analytics infused with Big Data can come to their rescue.
The term Big Data is really very big! Big Data can help draw crucial insights that would help financial institutions in analyzing their customer base and how their purchase decision patterns vary. It can also be used to enhance business results, especially in regard to credit risk management.
If you follow the current business news, in the coming three years, banks would be facing two major risks – Credit and Liquidity. However, if credit risk managers follow the below-mentioned ways, they can turn this complication into an opportunity:
- Data Analytics determines a person’s behavior and how his circumstances have changed. This is verified by his social media activity, which further affirms how his financial position has changed with time. Hence, the chances of fraud and non-repayment are put in check.
- With proper analysis of mobile and social media data, credit risk managers may be able to gather insights and broaden their market horizon, enhancing the market base.
- Data science can establish contact with low risk customers.
Parsing data with Python should always be discussed after getting a good grip on the nuances of machine learning because both the intricate concepts are interlaced with each other. Click on the link first pythonprogramming.net/downloads/intraQuarter.zip and then go forward with parsing the data.
The data set given in the above link resembles the data set we caught hold of when we first visited the webpages before. The point of interest here is that we don’t need to visit the page even. We just need to have the full HTML source code, that’s it! This system is quite similar to parsing the website without disturbing bandwidth use.
Things are not at all going right in the technological sphere. The domain is shrouded under the dark haze of WannaCry Ransomware this weekend. After the relaxing weekend, the Monday morning situation could never have been worse. The figures revealed on Monday evening by the Elliptic, a Bitcoin forensics firm, affirmed with the duty of keeping a close watch, confirmed a digit of $57,282, 23 shelled out to the hackers of Ransomware malware attack, who took over innumerable amount of computers worldwide on Friday and over the weekend.
The recent past has been witnessing the unprecedented malware attack across 150 countries. The current picture describes more than 200000 systems around the world being affected and the loss of tons of data.
Also read: How To Stop Big Data Projects From Failing?
A few years back also, Ransomware was unheard of and today it has emerged as one of the major issues of concern. So, what is the solution now? Several veteran data scientists and the honchos of the technological world have voted for Predictive Analysis as the ultimate solution for destroying Ransomware.
With the conventional cyber defense mechanisms at a backseat, Predictive Analysis defense technology remains the ultimate resort for any organization. The Predictive Analysis is mainly dependent on instituting a pattern of life within the enterprise and saving from disgruntling malware and similar disturbing activities.
Paul Brady, the CEO of Unitrends, explained the procedure where the backup system uses the tools of machine learning to identify and understand that certain data anomalies indicate the threat of a Ransomware attack.
So the above mentioned description clearly depicts the many advantages of Predictive Analysis. Now, the sad part of the story remains, that the difficulty in management remains the major blockage for the employment of this method. Let’s hope for the best and wait for the day when Predictive Analysis would be the only possible solution. Till then gather information on SAS predictive modeling training in Pune and Gurgaon only at www.dexlabanalytics.com
What is trending in the technical world? Big Data is the word. The sudden upsurge witnessed in the IT Industry has equivalently led to the emergence of Big Data. The complexities of the Data sets are extremely troublesome to co-ordinate activities with the usage of on-hand database management tools. Hence, the shift to this catchy phrase, dealing with homogenous amount of data and is of uttermost importance. Let’s have a quick tete-a-tete with this newest branch of science i.e. Big data Analytics.
- A for A/B Testing– A very essential element of web development and big data industry, it is a powerful evaluation tool to decide which version of an app or a webpage is extremely effective to meet the future business goals. Also, this decision is taken carefully after comparing the numerous versions to select the best from the rest.
- Set the standards for Associate Rule learning– The structure enlists a set of technique in the quest for interesting relationships or the ‘association rules’ amidst variables in massive databases. For better understanding refer to the flowchart attached in the blog, describing a market analysis by a retailer, assuming the products which are high on demand and the usage of this data for successful marketing.
- Get a better understanding of Classification Tree Analysis-In clearer terms, it is the method of recognizing the category in which the new observation falls into. Statistical Classification mainly implements to:
- Classification of organisms into groups.
- Automatically allocating documents into categories.
- Creating profiles of students enrolling for the online courses.
PS: For the better understanding, take a quick glance at the illustration attached below.
- Why would you opt for Data Fusion and Data Integration? The answer is simple. The blending of data from multiple sensors, data integration and fusion leads to the total accuracy and direct more specific inferences which otherwise wouldn’t have been possible from a single sensor alone.
- Mingling with Data Mining – To be precise, Data Mining is nothing but the collective data extraction techniques to be performed on a large chunk of data. The parameters include Association, Classification, Clustering and Forecasting.
- The cloning of Neural Networks- This includes Non-Linear predictive models for pattern recognition and optimization.
Interested in a career in Data Analyst?
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Do you know why several organizations face problems while implementing Big Data? Still wondering? The reason is lack of poor or non-existent data management strategies.
Proper technology systems need to be adopted. Without procedural flows, data is impossible to be analysed or delivered appropriately. However, before we delve deeper into making a plan to introduce data management strategies into the business, we should pay enough attention to the systems and technologies we are thinking to launch, along with the number of improvements to be made.
Big Data is ruling the tech world. Here are few types of tech that needs to be a part of a successful data management strategy:
Common data mining tools are R, SAS and KXEN.
More consistent, Automated ETL is used to extract, transform and load data.
They are efficient in offering a protective layer of security and quality assurance by doing a proper problem diagnosis and monitoring critical environments.
BI and Reporting Analytics
Turn data into insights, with BI and Reporting Analytics. It is very vital that data go to the right people and of course in the right manner. If that doesn’t happen, organizations suffer incessantly.
Analytics is a huge branch of study, starting from customer acquisition data, tracking details to intriguing user-friendly interfaces and product life cycle.
For More Details, Read The Full Blog Here:
Understanding The Core Components of Data Management
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The ‘trending’ topic this season is Data Processing. The statistics attached with this blog depicts that respondents have mainly voted for NoSQL and SQL databases. The opinions of the respondents have conferred the title of ‘most engaging’ to NoSQL database, confirming the second position with a 74.8%.
The survey declares the PostgreSQL as the confirmed winner, where 25.3 % have proclaimed it to be ‘very interesting’ and 37.7 % have confined within ‘interesting’.
Also read: Top Databases of 2017 to Watch Out For
- Elasticsearch declared runner-up with an overall 59%.
- The amalgamation of Lucene and Solr roaring with 43.8%
- More interest devoured in Apache Spark with 3%
- Hadoop scoring a meager of 8%
Next, it unveils that the US respondents have mainly opted for Elasticsearch to PostgreSQL, and Oracle have failed to evoke any interest in the mind of US respondents. However, the picture is completely opposite for the European respondents.
Also read: Data Analytics for the Big Screen
The ending note states that it is high time we realize that the dire need of the hour is data storage and processing. This conclusion is supported by the fact that so many respondents have invested their valuable time in the survey and clearly shows that database is here to stay.
Here, we will discuss about how to incorporate Oracle BI server and make use of the existing Subject Areas built on RPD in Tableau desktop workbook as Data Source.
NOTE: This is applicable for 8.2, 8.3, 9.0 and later versions.
Firstly, to launch an ODBC connection with the Oracle BI Server to access RPD Online, you have to install a copy of Oracle Business Intelligence Developer Client Tools (available from the Oracle website). Following, you can use the same DSN to connect to Tableau through ODBC connections.
As soon as you are done with installing the client, carry out the following steps to create a database source name (DSN):
- Follow the steps mentioned below to add a new system DSN for Oracle BI Server ODBC in the ODBC Administrator tool..
- Go to the System DSN tab and click Add
- Choose the Oracle BI Server DSN, among other available drivers
- Give correct connection details and connect to BI Server
3. Save the System DSN
In total, there are 3 levels in RPD:
- Physical Layer (right pane) – This is the layer where a connection is established between each data source and the raw tables are disclosed. Joins across tables are performed here.
- Business Layer (middle pane) – This is where logical relations, data modelling and hierarchy development are implemented.
- Presentation Layer (left pane) – This is the layer exposed to the business through “subject areas”. The subject areas are clearly modelled to display data in the most easy-to-understand format.
Under Tableau, tables in the presentation layer are adjoined as data sources, only.
Locate the Key Fields in Dimensions and Fact Table in Physical Layer of RPD and disclose the same to respective Presentation Tables in Presentation Layer of Sample Sales Lite Subject Area.
Create a “.tdc” file
Create a Tableau data source customization (.tdc) file to connect Tableau Desktop to an Oracle BI server.
- Open a text editor; copy and paste the below code onto it:
<connection-customization class=’genericodbc’ enabled=’true’ version=’9.3′><vendor name=’Oracle Business Intelligence’ />
<driver name=’Oracle BI Server 1′ />
<customization name=’CAP_SET_ISOLATION_LEVEL_VIA_ODBC_API’ value=’no’ />
<customization name=’CAP_SUPPRESS_DISCOVERY_QUERIES’ value=’no’ />
<customization name=’SQL_CATALOG_USAGE’ value=’0′ />
<customization name=’SQL_SCHEMA_USAGE’ value=’0′ />
<customization name=’CAP_FAST_METADATA’ value=’yes’ />
Nota bene: Version is crucial; adjust the version with that of your Tableau desktop version. In my case, it is 9.3.
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- Give correct connection details and connect to BI Server