FAQ

Can hive process semi structured data?

by Author

Can hive process semi structured data?

Hive performs ETL functionalities in Hadoop ecosystem by acting as ETL tool. Semi structured data such as XML and JSON can be processed with less complexity using Hive.

How do you deal with semi structured data?

10 Effective Ways to Deal with Structured and Semi-Structured…

  1. Using lexical analysis.
  2. Seeking out identifiers.
  3. Analyzing sentiment.
  4. Web scraping.
  5. Natural Language Processing (NLP)
  6. Pattern sensing.
  7. Predictive analytics.
  8. Avoid over-fitting:

Can pig handle unstructured data?

Another advantage of Pig is that it can easily work on raw data. Unlike other big data analytics tools, Pig is the most efficient tool that can work on any sort of unstructured, semi- structured, and structured data.

Can Hadoop deal with semi structured data?

Data in HDFS is stored as files. Hadoop does not enforce on having a schema or a structure to the data that has to be stored. This allows using Hadoop for structuring any unstructured data and then exporting the semi-structured or structured data into traditional databases for further analysis.

READ ALSO:   Is Whitebeard the best character in One Piece?

How mapper and reducer works in hive?

Map Reduce talk in terms of key value pair , which means mapper will get input in the form of key and value pair, they will do the required processing then they will produce intermediate result in the form of key value pair ,which would be input for reducer to further work on that and finally reducer will also write …

How does Hive process XML data?

Solution

  1. Step 1: Create a temp hive table. create table. CREATE EXTERNAL TABLE companyxml(xmldata STRING) LOCATION ‘/user/hive/companyxml/company.
  2. Step 2: Create View. load data.
  3. Step 3: Output. Let’s check how the view looks like:
  4. Step 4: Use UDF. a) Add JAR.
  5. Step 5: Validate output. hive> SELECT * FROM companyview;

What is semi-structured data used for?

Semi-structured data is a form of structured data that does not obey the tabular structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data.

How do pigs handle data?

READ ALSO:   What is the gender inequality in sports?

To analyze data using Apache Pig, programmers need to write scripts using Pig Latin language. All these scripts are internally converted to Map and Reduce tasks. Apache Pig has a component known as Pig Engine that accepts the Pig Latin scripts as input and converts those scripts into MapReduce jobs.

How do you get a pig out of grunt shell?

quit – Quit the grunt shell.

What is semi-structured data with example?

Semi-Structured Data Examples An example of semi-structured data is delimited files. It contains elements that can break down the data into separate hierarchies. Similarly, in digital photographs, the image does not have a pre-defined structure itself but has certain structural attributes making them semi-structured.

How does Hadoop handle unstructured data?

There are multiple ways to import unstructured data into Hadoop, depending on your use cases.

  1. Using HDFS shell commands such as put or copyFromLocal to move flat files into HDFS.
  2. Using WebHDFS REST API for application integration.
  3. Using Apache Flume.
  4. Using Storm, a general-purpose, event-processing system.

What is the difference between hive tool and pig tool?

Hive tool is used for structured data whereas pig is used for structured,semi-structured and unstructured data. To work with data basically import it to the hive/pig (from mysql or text etc into the hdfs) and then apply queries to analyse your data. FYI : Pig works with relations.

READ ALSO:   Was Hiruzen in his prime stronger than hashirama?

How do I process semi-structured data in pig?

A whole year to learn to program for the price of only 5 months of the monthly subscription. Semi-structured data can be process in PIG by performing some queries like ETL , once its get processed by pig and converted into structured form then we can upload it into HIVE for further processing like visualization,

What is the difference between Hadoop pig and Hive?

With that being said, Pig can handle unstructured data with no schema defined whereas Hive requires a schema.Also, in some cases Pig can also be used to connect data with a schema giving it an upper hand over Hive. In contrast, Hive converts Hadoop into a dataware house and acts like a SQL dialect.

Can pig handle unstructured data in data factory?

But, in the data factory, data may not be in a nice, standardized state yet. This makes Pig a good fit for this use case as well, since it supports data with partial or unknown schemas, and semi-structured or unstructured data. Would like to know more how Pig can handle unstructured data while Hive can’t.