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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]
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Cloudera Impala top features useful for developers

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Cloudera Impala that runs on Apache Hadoop. The program was proclaimed in October 2012 with a common beta trial dispersion. Popular usage is in data analytics.The key features useful for interviews.
Impala The Apache-licensed Impala program begets scalable collateral database techniques to Hadoop, authorizing consumers to subject low-latency SQL requests to information kept in HDFS and Apache HBase short of needing information motion either alteration.


Impala is amalgamated with Hadoop to employ the similar file and information setups, metadata, safeguarding and asset administration architectures applied by MapReduce, Apache Hive, Apache Pig and different Hadoop code.

Impala Applications

Impala is advanced for experts and information experts in science to accomplish systematic computational analysis of data or statistics on information kept in Hadoop through SQL either trade intellect implements. 

 
The effect is that extensive information handling (via MapReduce) and two-way requests may be completed on the similar configuration utilizing the similar information and metadata – eliminating the demand to wander information places in to specific setups and or exclusive setups plainly to accomplish examination. 


Features included
  • Supports HDFS#Hadoop_distributed_file_system|HDFS and Apache HBase storage
  • Reads Hadoop date setups, containing written material, LZO, SequenceFile, Avro and RCFile Supports Hadoop safeguarding (Kerberos authentication)
  • Fine-grained, Role-based allowance with Sentry Uses metadata, ODBC driver, and SQL structure as of Apache Hive

In first 2013, a column-oriented DBMS|column-oriented information setup named Parquet was proclaimed for designs containing Impala. In December 2013, Amazon Web Services proclaimed aid aimed at Impala.


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