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Poster - Practical

Practical
Course

Data Science and Analytics

9 Documents
Students shared 9 documents in this course
Academic year: 2022/2023
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Dr. Vishwanath Karad MIT World Peace University

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Apache Hive

Apache Hive is an open-source data warehousing and SQL-like query language

system built on top of Hadoop for processing and analyzing large datasets. It

provides a high-level interface for managing and querying structured and semi-

structured data in a distributed storage environment, making it a valuable tool

for big data processing and analytics. Hive uses a language called HiveQL,

which is similar to SQL, to interact with data stored in Hadoop's HDFS or other

distributed file systems.

Map-Reduce

Hive's extensibility is achieved through the

integration of user-defined functions (UDFs)

and custom scripting to enable custom data

processing and transformations.

System Architecture

SQL vsHadoop

SQL: Ideal for structured data, offers a standard

query language for relational databases, enabling

efficient data retrieval and manipulation.

Hadoop: Suited for big data with structured, semi-

structured, or unstructured data, uses a distributed

file system and batch processing, enabling

scalable storage and processing of vast datasets.

SQL: Provides ease of use, well-defined schemas,

and ACID transactions for traditional databases.

Hadoop: Offers flexibility for diverse data types

and massive scalability, but requires complex data

transformations and lacks real-time capabilities,

making it suitable for different use cases.

1.

2.

3.

4.

KEY FEATURES

Extensibility

Apache Hive offers a SQL-like query

language for managing and analyzing large

datasets in Hadoop. Its schema-on-read

approach allows flexible handling of

structured and semi-structured data. With

seamless integration into the Hadoop

ecosystem, it supports scalability,

optimization, and robust security features,

making it a vital component for big data

processing and analytics.

Was this document helpful?

Poster - Practical

Course: Data Science and Analytics

9 Documents
Students shared 9 documents in this course

University: Dr. Vishwanath Karad MIT World Peace University

Was this document helpful?
Apache Hive
Apache Hive is an open-source data warehousing and SQL-like query language
system built on top of Hadoop for processing and analyzing large datasets. It
provides a high-level interface for managing and querying structured and semi-
structured data in a distributed storage environment, making it a valuable tool
for big data processing and analytics. Hive uses a language called HiveQL,
which is similar to SQL, to interact with data stored in Hadoop's HDFS or other
distributed file systems.
Map-Reduce
Hive's extensibility is achieved through the
integration of user-defined functions (UDFs)
and custom scripting to enable custom data
processing and transformations.
System Architecture
SQL vsHadoop
SQL: Ideal for structured data, offers a standard
query language for relational databases, enabling
efficient data retrieval and manipulation.
Hadoop: Suited for big data with structured, semi-
structured, or unstructured data, uses a distributed
file system and batch processing, enabling
scalable storage and processing of vast datasets.
SQL: Provides ease of use, well-defined schemas,
and ACID transactions for traditional databases.
Hadoop: Offers flexibility for diverse data types
and massive scalability, but requires complex data
transformations and lacks real-time capabilities,
making it suitable for different use cases.
1.
2.
3.
4.
KEY FEATURES
Extensibility
Apache Hive offers a SQL-like query
language for managing and analyzing large
datasets in Hadoop. Its schema-on-read
approach allows flexible handling of
structured and semi-structured data. With
seamless integration into the Hadoop
ecosystem, it supports scalability,
optimization, and robust security features,
making it a vital component for big data
processing and analytics.

Students also viewed

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