Big data involves processing massive amounts of diverse information and delivering insights rapidly—often summed up by the four V's: volume, variety, velocity, and veracity. Data scientists and analysts need dedicated tools to help turn this raw information into actionable content, a potentially overwhelming task. Fortunately, some effective tools exist to make the task easier.

Hadoop is one of the most popular software frameworks designed to process and store Big Data information. Hive, in turn, is a tool designed for use with Hadoop. This article details the role of Hive in big data, as well as details such as Hive architecture and optimization techniques.

Let’s start by understanding what Hive is in Hadoop.

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What is Hive in Hadoop?

No one can better explain what Hive in Hadoop is than the creators of Hive themselves: "The Apache Hive™ data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. The structure can be projected onto data already in storage."

In other words, Hive is an open-source system that processes structured data in Hadoop, residing on top of the latter for summarizing Big Data, as well as facilitating analysis and queries.

Now that we have investigated what is Hive in Hadoop, let’s look at the features and characteristics.

Architecture of Hive



Hive chiefly consists of three core parts:

  • Hive Clients: Hive offers a variety of drivers designed for communication with different applications. For example, Hive provides Thrift clients for Thrift-based applications. These clients and drivers then communicate with the Hive server, which falls under Hive services.
  • Hive Services: Hive services perform client interactions with Hive. For example, if a client wants to perform a query, it must talk with Hive services.
  • Hive Storage and Computing: Hive services such as file system, job client, and meta store then communicates with Hive storage and stores things like metadata table information and query results.

Hive's Features

These are Hive's chief characteristics:

  • Hive is designed for querying and managing only structured data stored in tables
  • Hive is scalable, fast, and uses familiar concepts
  • Schema gets stored in a database, while processed data goes into a Hadoop Distributed File System (HDFS)
  • Tables and databases get created first; then data gets loaded into the proper tables
  • Hive supports four file formats: ORC, SEQUENCEFILE, RCFILE (Record Columnar File), and TEXTFILE
  • Hive uses an SQL-inspired language, sparing the user from dealing with the complexity of MapReduce programming. It makes learning more accessible by utilizing familiar concepts found in relational databases, such as columns, tables, rows, and schema, etc.
  • The most significant difference between the Hive Query Language (HQL) and SQL is that Hive executes queries on Hadoop's infrastructure instead of on a traditional database
  • Since Hadoop's programming works on flat files, Hive uses directory structures to "partition" data, improving performance on specific queries
  • Hive supports partition and buckets for fast and simple data retrieval
  • Hive supports custom user-defined functions (UDF) for tasks like data cleansing and filtering. Hive UDFs can be defined according to programmers' requirements

Limitations of Hive

Of course, no resource is perfect, and Hive has some limitations. They are:

  • Hive doesn’t support OLTP. Hive supports Online Analytical Processing (OLAP), but not Online Transaction Processing (OLTP).
  • It doesn’t support subqueries.
  • It has a high latency.
  • Hive tables don’t support delete or update operations.

How Data Flows in the Hive?

  1. The data analyst executes a query with the User Interface (UI).
  2. The driver interacts with the query compiler to retrieve the plan, which consists of the query execution process and metadata information. The driver also parses the query to check syntax and requirements.
  3. The compiler creates the job plan (metadata) to be executed and communicates with the metastore to retrieve a metadata request.
  4. The metastore sends the metadata information back to the compiler
  5. The compiler relays the proposed query execution plan to the driver.
  6. The driver sends the execution plans to the execution engine.
  7. The execution engine (EE) processes the query by acting as a bridge between the Hive and Hadoop. The job process executes in MapReduce. The execution engine sends the job to the JobTracker, found in the Name node, and assigns it to the TaskTracker, in the Data node. While this is happening, the execution engine executes metadata operations with the metastore.
  8. The results are retrieved from the data nodes.
  9. The results are sent to the execution engine, which, in turn, sends the results back to the driver and the front end (UI).

Since we have gone on at length about what Hive is, we should also touch on what Hive is not:

  • Hive isn't a language for row-level updates and real-time queries
  • Hive isn't a relational database
  • Hive isn't a design for Online Transaction Processing

As we have looked into what is Hive, let us learn about the Hive modes.

Hive Modes

Depending on the size of Hadoop data nodes, Hive can operate in two different modes:

  • Local mode
  • Map-reduce mode

User Local mode when:

  • Hadoop is installed under the pseudo mode, possessing only one data node
  • The data size is smaller and limited to a single local machine
  • Users expect faster processing because the local machine contains smaller datasets.

Use Map Reduce mode when:

  • Hadoop has multiple data nodes, and the data is distributed across these different nodes
  • Users must deal with more massive data sets

MapReduce is Hive's default mode.

Hive and Hadoop on AWS

Amazon Elastic Map Reduce (EMR) is a managed service that lets you use big data processing frameworks such as Spark, Presto, Hbase, and, yes, Hadoop to analyze and process large data sets. Hive, in turn, runs on top of Hadoop clusters, and can be used to query data residing in Amazon EMR clusters, employing an SQL language.

Hive and IBM Db2 Big SQL

Data analysts can query Hive transactional (ACID) tables straight from Db2 Big SQL, although Db2 Big SQL can only see compacted data in the transactional table. Data modification statement results won’t be seen by any queries generated in Db2 Big SQL until you perform a compaction operation, which places data in a base directory.

Hive vs. Relational Databases

Relational databases, or RDBMS, is a database that stores data in a structured format with rows and columns, a structured form called “tables.” Hive, on the other hand, is a data warehousing system that offers data analysis and queries.

Here’s a handy chart that illustrates the differences at a glance:

Relational Database


Maintains a database

Maintains a data warehouse

Fixed schema

Varied schema

Sparse tables

Dense tables

Doesn’t support partitioning

Supports automation partition

Stores normalized data

Stores both normalized and denormalized data

Uses SQL (Structured Query Language)

Uses HQL (Hive Query Language)

In order to continue our understanding of what Hive is, let us next look at the difference between Pig and Hive.

Pig vs. Hive

Both Hive and Pig are sub-projects, or tools used to manage data in Hadoop. While Hive is a platform that used to create SQL-type scripts for MapReduce functions, Pig is a procedural language platform that accomplishes the same thing. Here's how their differences break down:


  • Data analysts favor Apache Hive
  • Programmers and researchers prefer Apache Pig

Language Used

  • Hive uses a declarative language variant of SQL called HQL
  • Pig uses a unique procedural language called Pig Latin

Data Handling

  • Hive works with structured data
  • Pig works with both structured and semi-structured data

Cluster Operation

  • Hive operates on the cluster's server-side
  • Pig operates on the cluster's client-side


  • Hive supports partitioning
  • Pig doesn't support partitioning

Load Speed

  • Hive doesn't load quickly, but it executes faster
  • Pig loads quickly

So, if you're a data analyst accustomed to working with SQL and want to perform analytical queries of historical data, then Hive is your best bet. But if you're a programmer and are very familiar with scripting languages and you don't want to be bothered by creating the schema, then use Pig.

In order to strengthen our understanding of what is Hive, let us next look at the difference between Hive and Hbase.

Apache Hive vs. Apache Hbase

We've spotlighted the differences between Hive and Pig. Now, it's time for a brief comparison between Hive and Hbase.

  • HBase is an open-source, column-oriented database management system that runs on top of the Hadoop Distributed File System (HDFS)
  • Hive is a query engine, while Hbase is a data storage system geared towards unstructured data. Hive is used mostly for batch processing; Hbase is used extensively for transactional processing
  • Hbase processes in real-time and features real-time querying; Hive doesn't and is used only for analytical queries
  • Hive runs on the top of Hadoop, while Hbase runs on the top of the HDFS
  • Hive isn't a database, but Hbase supports NoSQL databases
  • Hive has a schema model, Hbase doesn't
  • And finally, Hive is ideal for high latency operations, while Hbase is made primarily for low-level latency ones

Hive Optimization Techniques

Data analysts who want to optimize their Hive queries and make them run faster in their clusters should consider the following hacks:

  • Partition your data to reduce read time within your directory, or else all the data will get read
  • Use appropriate file formats such as the Optimized Row Columnar (ORC) to increase query performance. ORC reduces the original data size by up to 75 percent
  • Divide table sets into more manageable parts by employing bucketing
  • Improve aggregations, filters, scans, and joins by vectorizing your queries. Perform these functions in batches of 1024 rows at once, rather than one at a time
  • Create a separate index table that functions as a quick reference for the original table.

Learn More About Hive and Hadoop

There is a lot to learn in the world of big data and this article on what is Hive has covered some of it. Simplilearn has many excellent resources to expand your knowledge in these fields. For instance, this article often referenced Hadoop, which may prompt you to ask, "But what is Hadoop?" You can also learn more through the Hadoop tutorial and Hive tutorial. If you want a more in-depth look at Hadoop, check out this article on Hadoop architecture.

Finally, if you're applying for a position working with Hive, you can be better prepared by brushing up on these Hive interview questions.

After going through this article on "what is Hive", you can check out this video to extend your learning on Hive -

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