The amount of data to be stored, monitored, and analyzed grows tremendously as a company grows. Queries on typical Database Warehouses will begin to take longer, making Data Management more challenging. With the development of Cloud Computing, the demand for Warehouse Solutions that can scale up to meet growing Data Storage and Analytical demands has become obvious, prompting enterprises to explore alternatives to traditional On-premise warehousing. 

In this article, you’ll look at the capacities of Redshift and Hive, as well as Hive vs Redshift Comparision in terms of pricing, performance, and convenience of use, so you can pick the best option for you.

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Hive vs Redshift: 10 Key Differences

1) Hive vs Redshift: Architecture

There are a variety of interfaces accessible, ranging from a web browser UI to a CLI to External Clients. The Apache Hive Thrift Server allows remote clients to use a number of programming languages to send instructions and requests to Apache Hive. Apache Hive’s central repository is a metastore that stores all information, including table definitions. The driver, which comprises a compiler, an optimizer to identify the optimum execution plan, and an executor, is the engine that enables Apache Hive work. Apache Hive can be operated with LLAP as an option. You can configure a metadata backup for high availability.

Since Redshift is a decentralized and clustered service, the data tables should be stored across numerous nodes. The number of slices for every node is determined by the type of node instance. Dense Compute (dc2), Dense Storage (ds2), and Managed Storage are the three types of instances that Redshift currently supports (ra3). In 1MB block, each slice stores many tables. This network of Slices and Nodes accomplishes two goals:

  • Distribute data and computation across all compute nodes in a uniform manner.
  • Collocate data & compute among nodes while decreasing data travel and enhancing join efficiency.

2) Hive vs Redshift: Availability

HiveServer2 has a mechanism called dynamic service discovery that allows several HiveServer2 instances to register with Zookeeper to offer highly available or load balancing. However, when using Amazon Redshift to interrogate the Amazon S3 data lake, you may create as many more clusters as you need, ensuring high availability and unbounded parallelism.

3) Hive vs Redshift: Performance

On the same dataset, tests have shown that Redshift is 5x to 20x faster than Hadoop Hive. Although Redshift is a Columnar Database, the data should always be organized, which means that querying any Unstructured Data Source will be faster. Furthermore, because Redshift is based on a massively Parallel Processing Architecture, the leader node is in charge of managing data distribution across the follower nodes in order to maximize performance.

Hadoop divides the task down to ensure completion, while Redshift just performs the query. Redshift is opting for the easier and faster route. Hadoop is going the more difficult but more reliable route. Hadoop is a Java Application Programming Interfaces (API)-based File System, whereas Redshift is a Relational Model Database Management System (RDBMS).

4) Hive vs Redshift: Cost

While it’s difficult to compare two completely distinct systems for all use scenarios, it appears like Redshift will be the less expensive alternative in the vast majority of cases. Pricing research for Redshift shows that if you follow the appropriate methods, you can test and run Redshift for a reasonable price. Running searches in Hadoop costs $200 per month, whereas redshift costs nothing. The cost is determined by the server’s location, but it is less than Hadoop. For instance, let’s say your monthly budget is $20.

5) Hive vs Redshift: Storage

Hive doesn’t provide storage; instead, it leverages a Database as a metastore to store metadata about the Records, Partitions, Views, Buckets, and other hive-created objects. The HDFS path /user/hive/warehouse stores the data imported into the hive database. If no location is given, all metadata is saved in this directory by default.

The data is saved in blocks of 64 or 128 MB in the HDFS path, whereas Redshift stores data in columns. The data is kept in a columnar format in conjunction with the design and architecture for query efficiency. For aggregations, the bulk of analytical queries will use a limited number of columns from a table. Instead of rows, data is kept in columns. This has a number of benefits for Redshift.

6) Hive vs Redshift: Security 

Apache Hive is connected with Hadoop Security, which employs Kerberos for Client-Server Mutual Authentication. The HDFS dictates permissions for newly formed files in Apache Hive, allowing for authorization by user, group, and others, whereas Amazon Redshift communicates with Amazon S3 or Amazon DynamoDB for Duplicate, UNLOAD, backup, and Restoration Operations using hardware-accelerated SSL to safeguard data in transit within the AWS Cloud.

7) Hive vs Redshift: Ease of Use

It only takes a few minutes to set up a Redshift Cluster. Redshift Database is fully maintained, fault resistant, and offers automated backups and quick restores as an AWS Cloud Service. Managing a Hadoop Cluster, on the other hand, may be a full-time task with a hosted Cloud solution like AWS EMR. While Hadoop, like Redshift, can enable automated backups, rapid restores, and other features, these are not included by default.

Any data warehousing effort will undoubtedly be a costly and high activity in Design, Development, Implementation, and Administration due to the knowledge required for a Hadoop implementation. When compared to automated backup and Data Warehouse Administration, Hadoop is more complex and difficult to manage.

8) Hive vs Redshift: Scalability

While Redshift has a maximum of 100 nodes and 16TB of storage per node, Redshift Spectrum allows you to store a somewhat indefinite amount of data in S3 for a low cost and query it only when needed. Scaling Hadoop, on the other hand, has essentially no bounds. In terms of scalability, both systems are considered fairly equal.

9) Hive vs Redshift: Data Transfer

Since Hadoop’s Hive complicated design, gathering data into its files system is difficult. On the other hand, Data sharing in Amazon Redshift enables the users to quickly share information for reading purposes across multiple Amazon Redshift Clusters even without hassles and delays that come with Datagram and Data Transfer.

10) Hive vs Redshift: Query Speed, Data Integration and Format

Hadoop takes 1491 seconds to process 1.2TB of data, which is significantly slower than Redshift. It is adaptable, with a local file system and any database that can handle any data format. Redshift, on the other hand, can execute 1.2TB of data in 155 seconds and can load it from Amazon S3 or DynamoDB dealing with  Strict data formats, such as CSV files. As a result, Hadoop is a better alternative for the user. Hadoop Hive may be integrated with a variety of providers, however, in this scenario, where Amazon is the lone vendor, Redshift offers no support. Hadoop is advantageous in this situation.


In this article, you got a deep understanding of Hive vs Redshift differences. The Apache Software Foundation created Hadoop, an Open-Source framework that focuses on Scalability, Dependability, and distributed Computing. Data Processing, Storage, Access, and Security are just some of the features available on the Hadoop Ecosystem. HDFS has a high throughput, which means it can simultaneously process large amounts of data. 

Amazon Web Services (AWS), a part of Inc., developed Redshift, a Cloud hosting web service. It’s used to build a large-scale Data Warehouse that’s hosted on the Cloud. When working with large datasets, Redshift is a fully managed and cost-effective Petabyte-scale Data Warehousing Solution. 

Hadoop falls short in terms of Performance Scalability, Service Prices, and Service costs, with the solitary benefit of easy interface with Third-party tools and products. Redshift wins in terms of ease of use, upkeep, and productivity. Due to its high availability and lower operational expenses compared to Hadoop, Redshift has recently experienced fast growth and appeal among Companies and Clients.  In case you want to export data from a source of your choice into your desired Database/destination such as Redshift then Hevo Data is the right choice for you! 

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Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations like Redshift, with a few clicks. Hevo Data with its strong integration with 150+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

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Share your experience of learning about the Hive vs Redshift! Let us know in the comments section below!

Principal Frontend Engineer, Hevo Data

With over a decade of experience, Suraj has played a crucial role in architecting and developing core frontend modules for Hevo. His expertise lies in building scalable UI solutions, collaborating across teams, and contributing to the open-source community, showcasing a deep commitment to innovation in the tech industry.

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