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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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5 HBase Vs. RDBMS Top Functional Differences

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Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform.

5 HBase Vs. RDBMS Top Functional Differences

5 HBase Vs. RDBMS Top Functional Differences


Here're the differences unlock now.

Random Accessing


HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database.

Database Rules


RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties.


HBase follows BASE properties and implements complex queries.
Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS.

Storage


From small to medium storage application there is the use of RDBMS that provides the solution with MySQL and PostgreSQL whose size increase with concurrency and performance. 


Codd's rules always need to keep in mind while extending the size of the database in the use of data processing.

Data Integrity


RDBMS focuses on and emphasizes consistency, referential integrity, abstraction from the physical layer, and complex queries through SQL language.

Takeaway

  • There is no single-point failure in HBASE. You always have backup data.
  • The server regions have the flexibility to share or rebalance the load among the servers.
  • Automatic partition helps to distribute its workload among servers. It happens with its in-built feature of HBASE.
  • The cost involved in the maintenance of HBASE is comparatively low.


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