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Mastering flat_map in Python with List Comprehension

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Introduction In Python, when working with nested lists or iterables, one common challenge is flattening them into a single list while applying transformations. Many programming languages provide a built-in flatMap function, but Python does not have an explicit flat_map method. However, Python’s powerful list comprehensions offer an elegant way to achieve the same functionality. This article examines implementation behavior using Python’s list comprehensions and other methods. What is flat_map ? Functional programming  flatMap is a combination of map and flatten . It transforms the collection's element and flattens the resulting nested structure into a single sequence. For example, given a list of lists, flat_map applies a function to each sublist and returns a single flattened list. Example in a Functional Programming Language: List(List(1, 2), List(3, 4)).flatMap(x => x.map(_ * 2)) // Output: List(2, 4, 6, 8) Implementing flat_map in Python Using List Comprehension Python’...
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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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