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15 Python Tips : How to Write Code Effectively

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 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth
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AWS EMR Vs. Hadoop: 5 Top Differences

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With Amazon Elastic MapReduce Amazon EMR, you can analyze and process vast amounts of data. It distributes the computational work across a cluster of virtual servers ( run in the Amazon cloud). An open-source framework of Hadoop manages it. 



AWS EMR Vs. Hadoop




Amazon EMR - Elastic MapReduce, The Unique Features


  • Amazon EMR has made enhancements to Hadoop and other open-source applications to work seamlessly with AWS.
  • For instance, Hadoop clusters running on Amazon EMR use EC2 instances as virtual Linux servers for the master and slave nodes, Amazon S3 for bulk storage of input and output data, and CloudWatch to monitor cluster performance and raise alarms.
  • Also, you can move data into and out of DynamoDB using Amazon EMR and Hive. That orchestrates by Amazon EMR control software that launches and manages the Hadoop cluster. This process is called an Amazon EMR cluster.


What does Hadoop do?


Hadoop uses a distributed processing architecture called MapReduce, in which a task maps to a set of servers for processing.


  • The results of the computation performed by those servers reduce to a single output set.
  • One node, designated as the master node, controls the distribution of tasks. The following diagram shows a Hadoop cluster with the master node directing a group of slave nodes which process the data.
  • One Master node handles multiple slave nodes. All open-source projects run on the Hadoop architecture can also be run on Amazon EMR. The most popular applications, such as Hive, Pig, HBase, DistCp, and Ganglia, are already integrated with Amazon EMR.


By running Hadoop on the Amazon EMR, you will get the following benefits of the cloud:


  1. The ability to provision clusters of virtual servers within minutes.
  2. You can scale the number of virtual servers in your cluster to manage your computation needs and only pay for what you use. 
  3. Integration with other AWS services.

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