What is Apache Hive? : Understanding Hive
Summary
TLDRApache Hive is a pivotal component of the Big Data landscape, developed by Facebook and now maintained by the Apache Software Foundation. It bridges the gap between traditional SQL and the Hadoop ecosystem by allowing users to execute SQL-like queries (HQL) on large datasets stored in HDFS. Hive is adept for OLAP and analytics but not suited for OLTP or real-time data processing due to inherent latency. It supports various file formats, offers compression techniques, and enables the use of UDFs. Unlike traditional RDBMS, Hive operates on a 'schema on read' model, suitable for handling massive data volumes with scalable storage on HDFS.
Takeaways
- 🐝 **What is Hive?** - Apache Hive is a data warehouse software that facilitates reading, writing, and managing large datasets in distributed storage using SQL-like queries.
- 🚀 **Development and Maintenance** - Originally developed by Facebook, Hive is now maintained by the Apache Software Foundation and widely used by companies like Netflix and Amazon.
- 🔍 **Purpose of Hive** - It was created to make Hadoop more accessible to SQL users by allowing them to run queries using a SQL-like language called HiveQL.
- 🧩 **Integration with Hadoop** - Hive interfaces with the Hadoop ecosystem and HDFS file system, converting HiveQL queries into MapReduce jobs for processing.
- 📊 **Use Cases for Hive** - It's ideal for OLAP (Online Analytical Processing), providing a scalable and flexible platform for querying large datasets stored in HDFS.
- ❌ **Limitations of Hive** - Not suitable for OLTP (Online Transaction Processing), real-time updates, or scenarios requiring low-latency data retrieval due to the overhead of converting Hive scripts to MapReduce jobs.
- 📚 **Features of Hive** - Supports various file formats, stores metadata in RDBMS, offers compression techniques, and allows for user-defined functions (UDFs) to extend functionality.
- 🔄 **Schema Enforcement** - Hive operates on a 'schema on read' model, which means it doesn't enforce schema during data ingestion but rather when data is queried.
- 📈 **Scalability** - Unlike traditional RDBMS, which typically have a storage capacity limit of around 10 terabytes, Hive can handle storage of hundreds of petabytes due to its integration with HDFS.
- 📋 **Comparison with RDBMS** - Hive differs from traditional RDBMS in that it doesn't support OLTP and enforces schema on read rather than on write, making it more suitable for big data analytics than transactional databases.
Q & A
What is Apache Hive?
-Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. It was originally developed by Facebook and is now maintained by the Apache Software Foundation.
Why was Hive developed?
-Hive was developed to provide a SQL-like interface (Hive Query Language or HQL) for users to interact with the Hadoop ecosystem, making it easier for those familiar with SQL to work with large volumes of data stored in Hadoop.
How does Hive complement the Hadoop ecosystem?
-Hive complements the Hadoop ecosystem by allowing users to run SQL-like queries on large datasets in Hadoop, which are then converted into MapReduce jobs for processing.
What are the main use cases for Hive?
-Hive is primarily used for OLAP (Online Analytical Processing), allowing for scalable, fast, and flexible data analysis on large datasets residing on the Hadoop Distributed File System (HDFS).
When is Hive not suitable for use?
-Hive is not suitable for OLTP (Online Transaction Processing), real-time updates or queries, or scenarios requiring low-latency data retrieval due to the inherent latency in converting Hive scripts into MapReduce jobs.
What file formats does Hive support?
-Hive supports various file formats including Sequence File, Text File, Avro, ORC, and RC file formats.
Where does Hive store its metadata?
-Hive stores its metadata in an RDBMS like Apache Derby, which allows for metadata management and query optimization.
What are some of the finest features of Hive?
-Hive features include support for different file formats, compression techniques, user-defined functions (UDFs), and specialized joins to improve query performance.
How does Hive's schema enforcement differ from traditional RDBMS?
-Hive enforces schema on read, meaning data can be inserted without checking the schema, which is verified only upon reading. Traditional RDBMS enforce schema on write, verifying data during insertion.
What is the storage capacity difference between Hive and traditional RDBMS?
-Hive can store hundreds of petabytes of data in HDFS, whereas traditional RDBMS typically have a storage capacity of around 10 terabytes.
Does Hive support OLTP operations?
-No, Hive does not support OLTP operations as it is designed for batch processing and analytics rather than transactional processing.
Outlines
💡 Introduction to Apache Hive
Apache Hive is a prominent data warehouse software project designed to facilitate querying and managing large datasets residing in distributed storage using SQL. Originally developed by Facebook, it is now overseen by the Apache Software Foundation. Hive serves as an interface for Hadoop, allowing users accustomed to SQL to interact with the Hadoop ecosystem seamlessly. It enables the execution of SQL-like queries, known as Hive Query Language (HQL), which are then translated into MapReduce or other processing frameworks like Tez or Spark for execution. The video script introduces Hive's purpose, its development history, and its adoption by major companies like Netflix and Amazon. It also sets the stage for further exploration of Hive's capabilities and its use cases in the context of the Hadoop ecosystem.
🚀 Hive's Features and Comparison with Traditional RDBMS
The script delves into the practical applications and limitations of Hive. It is highlighted as an optimal tool for Online Analytical Processing (OLAP) due to its scalability, speed, and flexibility. However, Hive is not suited for Online Transaction Processing (OLTP), real-time updates, or low-latency data retrieval scenarios. The script enumerates Hive's capabilities, such as support for various file formats, metadata storage in RDBMS like Derby, and compression techniques. It also touches on Hive's use of user-defined functions (UDF) and specialized joins to enhance query performance. A key differentiator between Hive and traditional RDBMS is the concept of 'schema on read' versus 'schema on write', with Hive being more flexible in data ingestion. The script concludes by contrasting Hive's ability to handle massive datasets with the more limited storage capacities of traditional RDBMS and sets the agenda for the next video, which will cover Hive installation.
Mindmap
Keywords
💡Apache Hive
💡Hadoop
💡Hive Query Language (HQL)
💡OLAP (Online Analytical Processing)
💡OLTP (Online Transaction Processing)
💡Schema on Read
💡Metadata
💡Compression Techniques
💡User-Defined Functions (UDF)
💡MapReduce
💡Spark
Highlights
Apache Hive is a popular data warehouse component in the Big Data landscape.
Hive was originally developed by Facebook and is now maintained by the Apache Software Foundation.
Hive is used by major companies like Netflix and Amazon.
Hive provides SQL-like interface to interact with the Hadoop ecosystem.
Hive was developed to ease the transition for organizations with traditional SQL-based data warehouses.
Hive allows users to write SQL-like queries called HQL to extract data from Hadoop.
Hive can be used for OLAP (Online Analytical Processing) and is scalable, fast, and flexible.
Hive is not suitable for OLTP (Online Transaction Processing) or real-time updates.
Hive supports various file formats like Sequence File, Text File, Avro, and ORC.
Hive stores metadata in RDBMS like Derby database.
Hive offers compression techniques for efficient data storage and retrieval.
Users can write SQL-like queries which Hive converts into MapReduce, Tez, or Spark jobs.
Hive supports User-Defined Functions (UDF) to extend query capabilities.
Hive offers specialized joins to improve query performance.
Hive enforces schema on read, allowing data insertion without immediate schema validation.
Hive can store hundreds of petabytes of data due to its integration with HDFS.
Traditional RDBMS enforces schema on write, verifying data during insertion.
Hive is not designed for OLTP, unlike traditional RDBMS systems.
The video concludes with a summary of Hive's development, use cases, features, and differences from traditional RDBMS.
Transcripts
what is hive
in this video you will get a quick
overview of Apache hive one of the most
popular data warehouse components on the
Big Data landscape it's mainly used to
complement the Hadoop file system with
its interface hive was originally
developed by Facebook and is now
maintained as Apache hive by Apache
Software Foundation it is used and
developed by biggies such as Netflix and
Amazon as well
let's start understanding how to do this
we'll need to get a grip on the
following things
why was I've developed how and when it
can be used
when hive cannot be used
finest features evolve
hive versus traditional RDBMS
why was hive developed
the Hadoop ecosystem is not just
scalable but also cost-effective when it
comes to processing large volumes of
data it is also a fairly new framework
that packs a lot of punch
however organizations with traditional
data warehouses are based on sequel with
users and developers that rely on sequel
queries for extracting data it makes
getting used to the Hadoop ecosystem an
uphill task and that is exactly why hive
was developed hive provides sequel
intellect so that users can write sequel
like queries called hql or hive query
language to extract the data from Hadoop
these sequel like queries will be
converted into MapReduce jobs by the
hive component and that is how it talks
to Hadoop ecosystem and HDFS file system
how and when hive can be used
hive can be used for OLAP online
analytic processing it is scalable fast
and flexible it is a great platform for
the sequel users to write sequel like
queries to interact with the large
datasets that reside on HDFS file system
when hive cannot be used
here is what hive cannot be used for
not a relational database it cannot be
used for OLTP online transaction
processing it cannot be used for
real-time updates or queries it cannot
be used for scenarios where low latency
data retrieval is expected because there
is a latency in converting the hive
scripts into MapReduce scripts by hive
some of the finest features of hive
it supports different file formats like
sequence file text file
Avro file format Oh RC file and RC file
metadata gets stored in our DBMS like
Derby database
I've provided a lot of compression
techniques queries on the compressed
data such as snappy compression and gzip
compression
users can write sequel like queries that
hive converts into MapReduce or tez or
sparks jobs to query against Hadoop
datasets
users can plug-in MapReduce scripts into
the hive queries using UDF user-defined
functions
specialized joins are available that
help to improve the query performance
if you don't understand any of the above
terms that's fine we will look into the
above features in detail in our upcoming
videos
five versus traditional RDBMS
hive enforces schema on read schema on
read allows the component to insert data
without checking for the type or schema
definition of the table it verifies the
data only when data is read from the
table
traditional RDBMS enforce schema on
right schema on right includes verifying
if the data is inserted as per the table
definition and schema definition during
the right phase itself this is how our
DBMS databases like my sequel or Oracle
servers work
hive allows you to store hundreds of
petabytes of data because hive stores
data in HDFS which has a scalable
storage space
our DBMS have a Mac storage capacity
around 10 terabytes of data and querying
such large data is not an easy task
five doesn't support OLTP
our DBMS supports OLTP
to quickly summarize in this video we
learned why hive was developed
we also learned how and where hive can
best work its magic and where it is not
such a great fit then we went through
some great features that come with hive
finally we explored the striking
difference between the hive and
traditional RDBMS
in the next video we will see how to
install hive
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