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Category: Big Data Analytics

Sure shot Ways to Crack Big Data Interviews

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Sure shot Ways to Crack Big Data Interviews

If you are a Big Data analyst looking for open position in the entry to mid level range of experience then you should prepare yourself with the following resources in your arsenal before you storm an interview with all guns blazing.

  • Adequate Expertise of Analytical tools like SAS for the processing of data

Make sure that you assign most of the time you have set aside for the preparation of your upcoming interview to brush up your knowledge regarding the tools of analytics that are relevant in your context. Ensure that you acquire proficiency in the analytics tool of your choice. For positions of junior levels the importance of expertise with a particular analytical tool like Hadoop, R or SAS cannot be overstressed. In such circumstances the focus centers around data preparation and processing. It is highly advisable that you review concepts related to the import and manipulation of data, the ability to read data even if it not standard say for example data whose input file types are multiple in number and mixed data formats. You also get to show off your skills at efficiently joining multiple datasets, selecting conditionally the observations or rows of data, how to go about heavy duty data processing of which SQL or macros are the most critical.

  • Make a Proper Review of End to End Business Process

This is most relevant towards candidates who have prior experience at working in the Big Data and Analytics industry. Prior experience inevitably gives rise to interviewers wanting to know more about the responsibilities that you shouldered and your role in the business process and how you fitted in the context of the broader picture. You should be able to convey to the interviewer that the data source is understood by you along with its processing and use.

  • A solid concept of the rudiments of statistics and algorithms

Again this tip is also for those with prior experience. Recruiters seek to know whether you are aware of issues likely to be faced by you while you confront problems regarding data and business. Even freshers are expected to know the fundamental concepts of statistics like rejection criteria, hypothesis testing outcomes, measures of model validation and the statistics related assumptions that a candidate must know about in order to implement algorithms of various sorts. In order to crack the interview you must be prepared with adequate knowledge of concepts related to statistics.

  • Prepare Yourself with At Least 2 Case Studies related to Business

The person on the other side of the interview table will undoubtedly try to make an assessment about your knowledge as far as business analytics is concerned and not solely to the proficiency you command in your tool of choice. Devote time to review projects on analytics you already have worked on if you have prior experience. Be prepared to elucidate on the business problem, the steps that were involved in the processing of data and the algorithm put into use in the creations of the models and reasons behind, and the way the results of the model was implemented. The interviewer might also ask about the challenges faced by you at any stage of the whole process, so keep in mind the issues faced by you in the past and their eventual resolution.

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  • Make Sure that Your Communication Remains Effective

If you are unable to effectively communicate then no much diligent preparations you make, they will be of no use. You can try out mock interviews and answering questions that the recruiter might ask. Spare yourself of the trouble of framing effective answers at the moment when the question is asked during an interview. Though you perhaps will be unable to anticipate each and every question, nevertheless but prior preparation will result in better and more coherent answers.

 

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The Pros and Cons of HIVE Partitioning

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The Pros and Cons of HIVE Partitioning Hive organizes data using Partitions. By use of Partition, data of a table is organized into related parts based on values of partitioned columns such as Country, Department. It becomes easier to query certain portions of data using partition.

Partitions are defined using command PARTITIONED BY at the time of the table creation.

We can create partitions on more than one column of the table. For Example, We can create partitions on Country and State.

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Syntax:

CREATE [EXTERNAL] TABLE table_name (col_name_1 data_type_1, ….)

PARTITIONED BY (col_name_n data_type_n , …);

Following are features of Partitioning:

  • It’s used for distributing execution load horizontally.
  • Query response is faster as query is processed on a small dataset instead of entire dataset.
  • If we selected records for US, records would be fetched from directory ‘Country=US’ from all directories.

Limitations:

  • Having large number of partitions create number of files/ directories in HDFS, which creates overhead for NameNode as it maintains metadata.
  • It may optimize certain queries based on where clause, but may cause slow response for queries based on grouping clause.

It can be used for log analysis, we can segregate the records based on timestamp or date value to see the results day wise / month wise.

Another use case can be, Sales records by Product –type , Country and month.

 

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Big Data and the Cloud- An Eclectic Mix

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Big Data and the Cloud- An Eclectic Mix
The FINRA or The Financial Industry Regulatory Authority, Inc. makes analysis of up to no less than 75 billion events each and every day. It is little wonder then that it finds its data center nearly filled to capacity. FINRA is looking forward to migrating to the cloud in order to continue to provide the protection for investors and continually respond to the market that it is famed for.

According to Matt Cardillo who is the Senior Director at FINRA, they are eyeing the elasticity that is enabled by cloud storage. He further continued also on their radar was an approach change in order to respond to market and volume data change along with changes in the behavior of users. Volatile markets result in usage spikes and also attract a whole lot more of users in their system.

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The surveillance program undertaken by FINRA performs analysis of data for suspicious activities as well as potential fraud. Their algorithms go through and analyze the data for any abnormalities or activities that might not be normal. They have in place alerts and exceptions that take stock of situations and then have access to analytics that help to determine if there is indeed a problem or whether it is a false call.

Stay Ahead of the Big Data Curve

Almost every day a new tool emerges to take stock of Analytics in the brave new world of Big Data Tech. According to Cardillo the kudos for staying ahead of the big data curve goes to the skilled staff at FINRA. He says that his people are innovative and are only too keen to embrace the latest advancements in technology. He confesses that after reverting to the cloud some of their present tech as well as tools will become irrelevant. But they are banking big on open source especially frameworks like Hive, Hadoop and Spark to get most out of the elasticity needed by their business.

 

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The Rise of the AI in Big Data

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The Rise of the AI in Big Data

The researchers working at the MIT “Computer Science and Artificial Intelligence Laboratory” or abbreviated simply as CSAIL are all set to make human intuition out of the analysis of big data equation by enabling computers to choose from the set of features that are put into use in order to identify patterns in the data that may be considered to be predictive. This is dubbed as the “Data Science Machine” and as things have progressed so far the software prototype has managed to beat 615 of 908 competing teams vying for the same ability across no less than three competitions of data science.

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Big Data may be considered as a complex and huge ecosystem that combines innovative processes from fields as diverse as storage, data analysis, curation, networking as well as search in addition to other functions and processes. As things stand much of analysis of big data is already algorithmic and automated but at the end of the day it is business users and data scientists who are needed in order to determine the particular dataset and analysis features which are required for visualization in the end and take action on the communicated data.

To put it simply at the end of the whole process humans are needed in order to make choices about data point combinations to chart out the relevant information.

The Data Science Machine is intended to naturally complement human intelligence and to make the most of the Big Data that is available for us waiting to be used.

The analysis of Big Data and Engineering of Features

As mentioned earlier actionable information lies at the hands of the big data scientist who is writing the code for analysis. It is this code that guides the analysis of the big data engine. In essence the advancement made by the MIT researchers is that not only does it serve to provide answers to questions regarding the data but also suggests additional questions accordingly.

This may be put into varied uses like to estimate the capacity of wind farms to generate power or making predictions about students who are likely to drop out of online courses.

5 Hottest Online Applications Inspired by Artificial Intelligence – @Dexlabanalytics.

The ultimate destination for all your data-related queries and assistance is DexLab Analytics. Being a premier Data Science training institute Gurgaon, DexLab Analytics takes pride in offering excellent data analytics courses for aspiring candidates.

 

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The Possibilities of Big Data

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It is no secret that Big Data has some wonderful applications that may change the way we interact with businesses, and even more how they interacts with us through other facets of this rapidly growing field. But, what can it do concretely? This blog post shares insights of this question.
 
The Possibilities of Big Data

Endless Possibilities of Big Data

 It can tell you what may most probably happen

Continue reading “The Possibilities of Big Data”

Big Data at Autodesk: 360 Degree view of Customers in the Cloud

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The last few years have seen a huge paradigm shift for many software vendors. The move away from a product-based model towards software-as-a-service (SAAS) in the cloud has brought huge changes. The main advantage of moving from a product based model to software-as-a-service is that the companies will be able to identify the service usage of how and why a product is being used. Earlier software companies used to run a survey or focus groups of customer feedback to identify the how and why a product is being used. This customer feedback survey has various limitations on identifying the product usage or where the product improvement has to be made.

Here’s All You Need to Know about Quantum Computing and Its Future

Autodesk was one of the frontrunners in the field, having been experimenting with the cloud based SAAS as far back as 2001 when it is acquired the BUZZSAW file sharing and synchronization service. Since then Microsoft, Adobe and many others moving into a subscription based, on-demand service and Autodesk has done the same with its core computer aided design products.

Software-as-a-service is a software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted. It is sometimes referred to as on-demand software. On-premise software is the exact opposite where the delivery of product is inside the particular organizations infrastructure.

Understanding how customers use a product is critical to giving them what they want. In a SAAS environment where everything is happening online and in the cloud, companies can gain a far more accurate picture  

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The idea of moving to cloud based subscription model gives the business to understand more about the product usage of customers. This gives them the edge to serve better to the customers. The shift in the industry shall not be ignored. Big Data is really being used now to understand how and where to improve the product.

 

The Indian IT industry is focusing mainly on Cloud, Analytics, Mobile and Social segment to further drive growth. This Software-as-a-service delivery model can certainly give the edge to do data analysis on where and how the product is used.

 

 

There are number of reasons why Software-as-a-service is beneficial to organizations:

 

  • No additional hardware costs, you can buy the processing power or hardware as per the requirement. Do not have to go for high end configuration as there is no requirement. Need based subscription.
  • Usage is scalable. You can scale whenever you require.
  • Applications can be customised.
  • Accessible from any location, rather than being restricted to installations on individual computers an application can be accessed from anywhere with an internet enabled device.

 

The adoption of cloud based delivery model is accelerating mainly because of the analytical capability it gives the business to understand the customers. Analytics rocks!.

 

For state of the art big data training in Pune, look no further than DexLab Analytics. It is a renowned institute that excels in Big data hadoop certification in Pune. For more information, visit their official site.

 

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How Vital Is It to Measure KPIs for Future Success

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How Vital Is It to Measure KPIs for Future Success

As I discussed earlier, Analytics is highly quantitative in nature. In this blog, we will discuss about the importance of Key Performance Indicators and how does KPIs help in measuring the organization’s performance and analytics.

Continue reading “How Vital Is It to Measure KPIs for Future Success”

Credit Risk Analytics and Regulatory Compliance – An Overview

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Credit Risk Analytics and Regulatory Compliance – An Overview

 

Post the Financial Crisis of 2008, there has been an increase in the regulatory vigilance of the capital adequacy of commercial banks across the globe. Banks need to be compliant with different regulatory capital requirements, so that they can continue their operations under situations of stress. A majority of analytical work in Indian BFSI domain is to provide analytical support to US based multinational NBFC’s. We would like to throw some light on the opportunities and scope of credit risk analytics in the US banking and financial services industry. The Federal Reserve requires the banks to be compliant with three main regulatory requirements: BASEL- II, Dodd Frank Act Stress Testing (DFAST) and Comprehensive Capital Analysis and Review (CCAR).

Continue reading “Credit Risk Analytics and Regulatory Compliance – An Overview”

Tips to Make Sense of All The Big Data Around Us, You Can Make a Difference

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Tips To Make Sense Of All The Big Data Around Us, You Can Make A Difference

We are all in the midst of the onslaught of information overload in many ways. We create it, transfer it and heartily participate in it. To get a grasp of the actual reality faced by businesses of all sizes, one needs to understand the exact scenario. According to IDC1, “The big data and analytics market will reach $125 billion worldwide in 2015” Further, IDC predicts, “Clearly IoT (Internet of Things) analytics will be hot, with a five-year CAGR of 30%.”

big-data-analytics

Data is created from all the posts made every second globally on social media, the humongous chatter, digital photo sharing, video uploads, online transactions, all the cell phone signals etc. – are all forms of data being generated leading to a massive information overload across servers and of course the cloud platforms.

All this digitization has led to a severe business challenge – so much big data, but how to make sense of all this? How does one use it for any kind of business related decision or direction? The following are some tips to help business make some sense from all this data right within their ambit.

1-Break it down

Big data remains big, unless methods are employed to break it into tiny usable groups of information. Eliminating, cross-referencing and grouping are the first steps to sort out various disparate data bytes.

2-Deduplication

There will always be the challenge of similar data springing up and being stored. Deduplication works as a primary point of ensuring that there is a reduction in the same data coming up for analysis.

3-Technology and its role

The role of specific technology cannot be ignored, when it comes to ensuring that all this big data is streamlined, stored safely and processed using the latest available techniques.

Big Data Landscape

4-Do not discard anything

Even the smallest and seemingly insignificant amount of information may be relevant and hold key insights.

5-Best practices for data analysis

The ecosystem revolving around the actual analysis of the big data needs to evolve into a more standardized format to be used across flexible structures leading to quicker outputs, better results and arriving at useful insights.

6-Having the right talent

This is one of the most important aspects, when it comes to actually making sense of all the data lying around across organizations. This is where trained and certified big data analysts appear.

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