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.
Table of Contents
What is Redshift?
Amazon Redshift is a fully managed Petabyte-scale Cloud Data Warehouse tool for storing and analyzing Big Data sets. Amazon Redshift performs Large-scale database migrations. The Column-oriented database in Redshift is built to link to SQL-based clients and BI tools, allowing users to access data in real-time. Redshift, which is based on PostgreSQL 8, provides quick functionality and efficient querying to help teams make informed business decisions.
For further information on Amazon Redshift, you can follow the Official Documentation.
What is Hive?
Hive, on the other hand, is an ETL and Data Warehousing Solution built on the Hadoop Distributed File System (HDFS). Hive is distinguished by its ability to query big datasets with a SQL-like interface using Apache Tez or MapReduce. Hive is a fault-tolerant Data Warehouse solution that provides massive-scale analytics using SQL. It enables users to read, produce, and manage Petabytes of data. Hive makes it simple to carry out tasks such as these.
- Data Encapsulation
- Ad-hoc Queries
- Big Data Analysis
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Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up Data Integration for 100+ Data Sources (including 40+ Free sources) and will let you directly load data from sources to a Data Warehouse or the Destination of your choice like Redshift. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
Let’s look at some of the salient features of Hevo:
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- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms FTP/SFTP, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks; and MySQL, SQL Server, TokuDB, MongoDB, PostgreSQL Databases to name a few.
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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 Amazon.com 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 100+ 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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