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Best Practices for Handling Duplicate Elements in Python Lists

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Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions.  01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list:     if item not in unique_list:         unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All three methods will result in uni
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Big Data:Top Hadoop Interview Questions (2 of 5)

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Frequently asked Hadoop interview questions.


1. What is Hadoop?Hadoop is a framework that allows users the power of distributed computing.

2.What is the difference between SQL and Hadoop?

SQL is allowed to work with structured data. But SQL is most suitable for legacy technologies. Hadoop is suitable for unstructured data. And, it is well suited for modern technologis.
Hadoop

3. What is Hadoop framework?

It is distributed network of commodity servers(A server can contain multiple clusters, and a cluster can have multiple nodes)

4. What are 4 properties of Hadoop?

Accessible-Hadoop runs on large clusters of commodity machinesRobust-An assumption that low commodity machines cause many machine failures. But it handles these tactfully. Scalable-Hadoop scales linearly to handle larger data by adding more nodes to the cluster. Simple-Hadoop allows users to quickly write efficient parallel code

5. What kind of data Hadoop needs?

Traditional RDBMS having relational structure with data resides in tables. In Hadoop. data should be in Key,Value pair.

6. Is Hadoop suitable for on the fly processing?

Hadoop is not suitable. It is suitable only for off-line processing. That means, we can not use Hadoop on active web logs. We can use it on web logs data,which already generated. So, in this property Hadoop is matching to traditional data warehouses.

7. What is Map reduce?

Map reduce is a data processing model, which contain mappers, and reducers. It takes unstructred data as input, and create as Key,Value pairs for processing on Hadoop.

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