In Hive, tables and databases are created first and then data is loaded into these tables. Hive as data warehouse designed for managing and querying only structured data that is stored in tables. While dealing with structured data, Map Reduce doesn't have optimization and usability features like UDFs but Hive framework does.

1. BY
G.KARTHIGA M.SC IT
NADAR SARASWATHI COLLEGE OF
ARTS AND SCIENCE, THENI.

2. INTRODUCTION
 Hive is a data warehouse infrastructure tool to
process structured data in Hadoop.
 It resides on top of Hadoop to summarize Big
Data, and makes querying and analyzing easy.
 Initially Hive was developed by Facebook,
later the Apache Software Foundation took it
up and developed it further as an open source
under the name Apache Hive

3. FEATURES OF HIVE
 It stores schema in a database and processed
data into HDFS.
 It is designed for OLAP.
 It provides SQL type language for querying
called HiveQL or HQL.
 It is familiar, fast, scalable, and extensible.

4. HIVE VS RELATIONAL
DATABASES
 In RDBMS the maximum data size allowed will
be in 10s of Terabytes whereas Hive has 100s
of Petabytes
 Hive is very easily scalable at low cost but
RDBMS is not scalable at low cost.
 In RDBMS, record level updates, inserts and
deletes, are possible whereas these are not
allowed in Hive

5. ARCHITECTURE OF HIVE
 3 major components:
• hive clients
• hive services
• Meta Store
 Under Hive Client:
• Thrift client
• ODBC driver
• JDBC driver

6. HIVE APPLICATIONS
 Log processing
 Text mining
 Document indexing
 Customer-facing business intelligence
(e.g., Google Analytics)
 Predictive modeling, hypothesis testing

7. HIVE COMPONENTS
 Shell: allows interactive queries like
MySQL shell connected to database
– Also supports web and JDBC clients
 Driver: session handles, fetch, execute
 Compiler: parse, plan, optimize
 Execution engine: DAG of stages (M/R,
HDFS, or metadata)
 Metastore: schema, location in HDFS,
SerDe

8. DATA MODEL
 Tables;
1) Typed columns (int, float, string, date,
boolean)
2) Also, list: map (for JSON-like data)
 Partitions
e.g., to range-partition tables by date
 Buckets
 Hash partitions within ranges (useful for
sampling, join optimization)

9. HIVE- DATA TYPES
 All the data types in hive are
classified into four types
1) Column Types
2) Literal
3) Null Values
4) Complex Types

10. CONCLUSIONS
 Supports rapid iteration of ad-hoc queries.
 Can perform complex joins with minimal
code.
 Scales to handle much more data than
many similar systems.