Featured Post

Python Regex: The 5 Exclusive Examples

-
Image
 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result = re.search(pattern, text) if result:     print("Pattern found:", result.group()) Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns:     if re.search(pattern, text):         print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit   import re text = "The price of the
Post a Comment

Big Data:Top Hadoop Interview Questions (1 of 5)

-
Looking out for Hadoop Interview Questions that are frequently asked by employers? Here is the first list of Hadoop Interview Questions which  covers HDFS…

hadoop 1 0f 5


1. What is BIG DATA?
Big Data is nothing but an assortment of such a huge and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques.

2. Can you give some examples of Big Data?
There are many real life examples of Big Data! Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade data per day, a jet airline collects 10 terabytes of censor data for every 30 minutes of flying time. All these are day to day examples of  Big Data!

3. Can you give a detailed overview about the Big Data being generated by Facebook?
As of December 31, 2012, there are 1.06 billion monthly active users on facebook and 680 million mobile users. On an average, 3.2 billion likes and comments are posted every day on Facebook. 72% of web audience is on Facebook. And why not! There are so many activities going on facebook from wall posts, sharing images, videos, writing comments and liking posts, etc.  In fact, Facebook started using Hadoop in mid-2009 and was one of the initial users of Hadoop.

4. According to IBM, what are the three characteristics of Big Data?
According to IBM, the three characteristics of Big Data are:
Volume: Facebook generating 500+ terabytes of data per day.
Velocity: Analyzing 2 million records each day to identify the reason for losses.
Variety: images, audio, video, sensor data, log files, etc.

5. How Big is ‘Big Data’?
With time, data volume is growing exponentially. Earlier we used to talk about Megabytes or Gigabytes. But time has arrived when we talk about data volume in terms of terabytes, petabytes and also zettabytes! Global data volume was around 1.8ZB in 2011 and is expected to be 7.9ZB in 2015. It is also known that the global information doubles in every two years!

6. How analysis of Big Data is useful for organizations?
Effective analysis of Big Data provides a lot of business advantage as organizations will learn which areas to focus on and which areas are less important. Big data analysis provides some early key indicators that can prevent the company from a huge loss or help in grasping a great opportunity with open hands! A precise analysis of Big Data helps in decision making!

For instance, nowadays people rely so much on Facebook and Twitter before buying any product or service. All thanks to the Big Data explosion.

7. Who are ‘Data Scientists’?
Data scientists are soon replacing business analysts or data analysts. Data scientists are experts who find solutions to analyze data. Just as web analysis, we have data scientists who have good business insight as to how to handle a business challenge. Sharp data scientists are not only involved in dealing business problems, but also choosing the relevant issues that can bring value-addition to the organization.

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

Explained Ideal Structure of Python Class

-
Image
When you are designing a class, you need to ensure that the classification of its critical parts is outlined at the beginning. The clearer the initial design, the more performant and scalable the class is. Some of the components in the order in which they should be defined in the class are mentioned as follows. Ideal structure of a class Class variables Constants or default variables are usually defined at the top of the class. For someone who is reading the code, it comes as an easy-to-view consolidated list, and for the interpreter it ensures that all such variables are processed before diving into the main logic of the class, including any other Instance method or constructor. The __init__ method The __init__ method provides information about inputs needed and how to instantiate the class. It is also the constructor of the class, which the very first method called while initializing the class . Special Python methods These methods change the functionality of the class or provide add
Read more

6 Python file Methods Real Usage

-
Image
Files always have data in them. For different purposes, you need methods to deal with them. These six file methods of use in this case. Related:  5 top file modes in python. Python file methods Below are the top six file methods in python: Tell() Seek() Flush() Fileno() truncate() isatty() 1. tell() The usage of tell method is it  returns the cursor-position of the file-pointer from the beginning of the file. It tells the current cursor position. The position starts from zero, which is same as of an index. Syntax:  fileobject.tell() 2. seek() The usage of seek  method is cursor position, from one position to the specified position from the beginning of the file, it moves. Here, the cursor will be sought to a particular location. Syntax:  fileobject.seek(position) 3. flush() The usage of flush  method is clean out the internal buffer. Before writing the text in the file, it is best practice to clear out that the internal buffer can be cleared. Syntax:  fileobject.flush() 4. fileno() The
Read more

How to Decode TLV Quickly

-
Image
TLV format contains three parts Tag, Length, and value. In a credit card or financial transactions, the TLV protocol supports this format. Below, you will find the ideas to decode TLV data quickly.  According to IBM , the tag tells what type of data it is. The length field denotes the length of the value. The value-field denotes the actual value. Structure of TLV. TLV comprises three field values. Tag Length Value How to Decode TLV The EMV labs developed tags which in turn part of EMV protocol. Each tag has an unique meaning. And the Tag and Length together takes 1 to 4 bytes of memory. 1. The Best example for TLV. Below is the way to decode the EMV tag. The first part of the TLV format is TAG. The second part is LENGTH, and finally the VALUE. Syntax of EMV tag: [Tag][Value Length][Value] (ex. " 9F40 05 F000F0A001 ") where,  Tag Name =  9F40 Value Length (in bytes) =  05  Value (Hex representation of bytes. Example, "F0" – 1-byte) =  F000F0A001 Finally, what is 9F40
Read more