Prerequisites – Introduction to Hadoop, Computing Platforms and Technologies
Apache Hive is a data warehouse and an ETL tool which provides an SQL-like interface between the user and the Hadoop distributed file system (HDFS) which integrates Hadoop. It is built on top of Hadoop. It is a software project that provides data query and analysis. It facilitates reading, writing and handling wide datasets that stored in distributed storage and queried by Structure Query Language (SQL) syntax. It is not built for Online Transactional Processing (OLTP) workloads. It is frequently used for data warehousing tasks like data encapsulation, Ad-hoc Queries, and analysis of huge datasets. It is designed to enhance scalability, extensibility, performance, fault-tolerance and loose-coupling with its input formats.
Initially Hive is developed by Facebook and Amazon, Netflix and It delivers standard SQL functionality for analytics. Traditional SQL queries are written in the MapReduce Java API to execute SQL Application and SQL queries over distributed data. Hive provides portability as most data warehousing applications functions with SQL-based query languages like NoSQL.
Apache Hive is a data warehouse software project that is built on top of the Hadoop ecosystem. It provides an SQL-like interface to query and analyze large datasets stored in Hadoop’s distributed file system (HDFS) or other compatible storage systems.
Hive uses a language called HiveQL, which is similar to SQL, to allow users to express data queries, transformations, and analyses in a familiar syntax. HiveQL statements are compiled into MapReduce jobs, which are then executed on the Hadoop cluster to process the data.
Hive includes many features that make it a useful tool for big data analysis, including support for partitioning, indexing, and user-defined functions (UDFs). It also provides a number of optimization techniques to improve query performance, such as predicate pushdown, column pruning, and query parallelization.
Hive can be used for a variety of data processing tasks, such as data warehousing, ETL (extract, transform, load) pipelines, and ad-hoc data analysis. It is widely used in the big data industry, especially in companies that have adopted the Hadoop ecosystem as their primary data processing platform.
Components of Hive:
- HCatalog –
It is a Hive component and is a table as well as a store management layer for Hadoop. It enables user along with various data processing tools like Pig and MapReduce which enables to read and write on the grid easily.
- WebHCat –
It provides a service which can be utilized by the user to run Hadoop MapReduce (or YARN), Pig, Hive tasks or function Hive metadata operations with an HTTP interface.
Modes of Hive:
- Local Mode –
It is used, when the Hadoop is built under pseudo mode which has only one data node, when the data size is smaller in term of restricted to single local machine, and when processing will be faster on smaller datasets existing in the local machine.
- Map Reduce Mode –
It is used, when Hadoop is built with multiple data nodes and data is divided across various nodes, it will function on huge datasets and query is executed parallelly, and to achieve enhanced performance in processing large datasets.
Characteristics of Hive:
- Databases and tables are built before loading the data.
- Hive as data warehouse is built to manage and query only structured data which is residing under tables.
- At the time of handling structured data, MapReduce lacks optimization and usability function such as UDFs whereas Hive framework have optimization and usability.
- Programming in Hadoop deals directly with the files. So, Hive can partition the data with directory structures to improve performance on certain queries.
- Hive is compatible for the various file formats which are TEXTFILE, SEQUENCEFILE, ORC, RCFILE, etc.
- Hive uses derby database in single user metadata storage and it uses MYSQL for multiple user Metadata or shared Metadata.
Features of Hive:
- It provides indexes, including bitmap indexes to accelerate the queries. Index type containing compaction and bitmap index as of 0.10.
- Metadata storage in a RDBMS, reduces the time to function semantic checks during query execution.
- Built in user-defined functions (UDFs) to manipulation of strings, dates, and other data-mining tools. Hive is reinforced to extend the UDF set to deal with the use-cases not reinforced by predefined functions.
- DEFLATE, BWT, snappy, etc are the algorithms to operation on compressed data which is stored in Hadoop Ecosystem.
- It stores schemas in a database and processes the data into the Hadoop File Distributed File System (HDFS).
- It is built for Online Analytical Processing (OLAP).
- It delivers various types of querying language which are frequently known as Hive Query Language (HVL or HiveQL).
Scalability: Apache Hive is designed to handle large volumes of data, making it a scalable solution for big data processing.
Familiar SQL-like interface: Hive uses a SQL-like language called HiveQL, which makes it easy for SQL users to learn and use.
Integration with Hadoop ecosystem: Hive integrates well with the Hadoop ecosystem, enabling users to process data using other Hadoop tools like Pig, MapReduce, and Spark.
Supports partitioning and bucketing: Hive supports partitioning and bucketing, which can improve query performance by limiting the amount of data scanned.
User-defined functions: Hive allows users to define their own functions, which can be used in HiveQL queries.
Limited real-time processing: Hive is designed for batch processing, which means it may not be the best tool for real-time data processing.
Slow performance: Hive can be slower than traditional relational databases because it is built on top of Hadoop, which is optimized for batch processing rather than interactive querying.
Steep learning curve: While Hive uses a SQL-like language, it still requires users to have knowledge of Hadoop and distributed computing, which can make it difficult for beginners to use.
Lack of support for transactions: Hive does not support transactions, which can make it difficult to maintain data consistency.
Limited flexibility: Hive is not as flexible as other data warehousing tools because it is designed to work specifically with Hadoop, which can limit its usability in other environments.