AWS Redshift Database in 2022: A Comprehensive Guide

on Data Warehouse • August 24th, 2020 • Write for Hevo


With the involvement of the Internet in our day-to-day activities, it isn’t easy to manage the data with the standard databases. We need more computation power and more robust systems to analyze these data. 

If you or your organization are facing such problems or planning to move to the AWS Redshift database, this blog post will help you in understanding all about the AWS Redshift Database and its features. 

Table Of Contents

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Hevo: Redshift Database

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What is Redshift Database?

AWS Redshift Database is a fully-managed cost-effective cloud-based data warehouse provided by Amazon as a Web service. AWS Redshift is designed to store petabytes of data and can perform real-time analysis to generate insights.

Redshift is a column-oriented database, which means it stores the data in columnar format that increases the performance while reading and writing of the data. Amazon Redshift data warehouse contains a collection of compute engines that form a cluster for computing and generating critical insights from the data. 

AWS Redshift is a very cost-effective data warehouse that is available on the cloud, and it roughly costs $1000/TB/Year, which is very very less when compared to a traditional data warehouse. It provides high-performance and high-quality analytical service that helps you to turn raw data into some meaningful insights. It possesses the modern concept of data warehousing such as columnar storage, data compression, and zone mapping to reduce the size of I/O, which in turn reduces the cost.

What are the Key Features of AWS Redshift Database?

AWS Redshift database provides a fully managed cloud data warehouse solution. Let’s have a look at some of the critical features that AWS Redshift offers: 

Column-oriented databases

  1. Data can be stored either in a row or column format. The most common way to store the data is in the row format, and many traditional databases are designed in such a fashion. Storing the data in a row format allows you to execute a large number of small operations, typically known as OLTP (Online Transaction Processing).
  2. Redshift is a column-oriented database that means it stores the data in a columnar fashion which increases the speed of data processing when it comes to accessing large volumes of data, typically known as OLAP. In Redshift, users generally execute small queries on large datasets to generate critical insights from the data. AWS Redshift, being a column-oriented database, processes a job that contains a massive amount of data very quickly.

Massively parallel processing (MPP)

Redshift database works on the Massive Parallel Processing concept, which is a divide-and-conquer strategy. Large datasets or large processing jobs are divided and distributed among a cluster of compute engines for parallel processing. As each compute node has its computational power, the tasks are processed simultaneously, which results in a significant reduction of processing time.

Data Encryption

  1. AWS has a centralized encryption system known as AWS KMS (Key Management Service) that provides the utmost security to the data at rest and data in flight. Encryptions in AWS Redshift are highly robust and customizable. 

Private Network

  1. AWS lets you isolate your network with the help of Virtual Private Cloud (VPC). For businesses that want additional security, they can choose to separate their system within Redshift by enabling the Amazon VPC. The user’s data warehouse remains connected to the existing IT infrastructure with IPsec VPN.

Fault tolerance

  1. Fault tolerance – AWS Redshift has excellent support for the replication of data. AWS continuously monitors its cluster, and when any drives, nodes or cluster fails, it replicates the data to other healthy nodes, thereby providing the fail-safe mechanism to access data.

Concurrency limits

  1. Concurrency limits – AWS provides you with the flexibility to choose the concurrency limits. You can either select fixed-size clusters or on-demand clusters. On-demand clusters automatically scale up and down based on the data loads. Redshift maintains concurrency limits that are similar to other data warehouses, but with a degree of flexibility.

How to Use the Redshift Database?

Organizations with a high demand for analytics and data access are one of the best places to use Redshift databases.

Different departments and teams can have their own node and easily access others thanks to its vertical design for clusters, which prevents wait times from rising or creating bottlenecks.

Redshift could be used in the financial services industry to analyze historical market data or to develop predictive models.
Redshift can be useful for businesses with fluctuating data requirements. Companies can quickly transition from gigabyte-level storage to petabyte-level storage thanks to the on-demand activation and deactivation of nodes.

The platform is frequently used to store log data for analysis, including web logs, clickstream data, and other information. This is helpful for both UX design and marketing and online advertising.
Redshift databases can be used in business intelligence to create numerous customised dashboards and improve ad hoc analysis.

Thanks to a variety of connectors and compatibility with SQL and several other database client languages, companies that gather data from various sources and channels can also gain from Redshift’s modular design.

When to use the AWS Redshift Database?

Amazon Redshift is used when the data to be analyzed is humongous. Redshift can only be a practical solution if the data is at least a petabyte-scale (1015 bytes). Only at that scale can Redshift make use of the MPP technology. There are some particular use cases that justify its use in addition to the size of the data.

  • Real-time analytics:

Numerous businesses frequently need to implement solutions quickly and base decisions on real-time data. Uber is a good example.

Uber must make decisions quickly based on historical and current data. It must make decisions regarding surge pricing, where to dispatch drivers, what route to take, anticipated traffic, and a variety of other factors.

A company like Uber, which has operations all over the world, must make thousands of these decisions every minute. To make those decisions and guarantee efficient operations, both the current data stream and historical data must be processed. Redshift can be used in these situations as the MPP technology to speed up data access and processing.

  • Combining multiple data sources: In some cases, processing structured, semi-structured, and/or unstructured data is necessary to glean insights. The diverse data structures from various sources cannot be handled by traditional business intelligence tools. In these use cases, Amazon Redshift is a powerful tool.
  • Business intelligence:

Many different individuals must manage an organization’s data. They won’t all be experts in the programming languages that engineers use and they won’t all be data scientists.

They can rely on comprehensive reports and user-friendly information dashboards. Redshift can be used to build highly functional dashboards and automatically generate reports. It can be used with third-party tools made by AWS partners as well as tools like Amazon Quicksight.

  • Log analysis:

A potent source of insightful data is behaviour analytics. Behavior analytics offer details on how a user interacts with an application, how long they use it for, how many times they click on things, and a wide range of other data.

To gain insight into user behaviour, the data can be gathered from a variety of sources, including a web application used on a desktop, mobile device, or tablet. Redshift can be used to combine complex datasets and computing data. Traditional data warehousing can also be done with Redshift. But for that, programmes like the S3 data lake would probably be more appropriate. Redshift can be used to process data in S3 and save the results in Redshift or S3.

Redshift Database vs. Traditional Database

AWS Redshift is a SQL data warehouse with additional capabilities than traditional database/data warehouses. The below side-by-side comparison will help you understand the key differences between AWS Redshift and a Traditional SQL Database.

ParameterAWS Redshift DatabaseTraditional Database
Data Storage TypeAWS Redshift stores the data in a columnar format that has exceptional IO speed.Traditional databases like SQLServer, MySQL, etc store the data in row format and have poorer performance when compared to Redshift.
CompressionRedshift database compresses the data while storing thereby reducing the cost of data scanning.Typically traditional databases don’t offer such compression techniques.
Cloud-EnabledRedshift is a fully-managed cloud service. Hence you need not set up and maintain the hardware.Traditional databases are generally on-premise and the cost of hardware setup and maintenance is quite high.
PricingRedshift offers a scalable architecture and you have to pay for what you use.Traditional databases require an on-premise setup of hardware and it has a high initial setup cost.
Huge Data ProcessingRedshift database can effectively process huge volumes of data and it automatically manages the scaling up and down of the cluster.Performance on huge data queries is poor when compared to Redshift. You need to scale the cluster that requires downtime of certain applications manually.

What is AWS Redshift Architecture?

AWS Redshift Architecture is pretty straightforward. It contains a leader node and several compute nodes to perform the analytics. Below are the schematics of AWS Redshift architecture: 

Redshift Database: Redshift Architecture | Hevo Data

AWS Redshift connects to client applications via JDBC connection, and it has support for major programming languages (Python, Scala, Java, Ruby, etc.)

Leader Node

The leader node acts as a bridge between the client and the compute nodes. It manages to and fro communications from the client and compute nodes. It parses the client’s input and prepares the plan that will be executed on the cluster nodes. Based on the execution plan, the leader node compiles code, distributes the compiled code to the compute nodes, and assigns a portion of the data to each compute node.

Compute Nodes

Once the leader node sends the SQL or the compiled code to the compute nodes, the compute node performs the computation and generates the desired results, and sends them back to the leader node for aggregations.

Client applications

Business intelligence (BI) reporting, data mining, and analytics tools, as well as a variety of data loading and ETL (extract, transform, and load) tools, are all integrated with Amazon Redshift. Since Amazon Redshift is built on the widely used PostgreSQL database, most current SQL client applications can be used with little to no modification.


A cluster is the main piece of infrastructure for an Amazon Redshift data warehouse.

One or more compute nodes make up a cluster. A second leader node manages external communication and coordinates the compute nodes when a cluster has two or more compute nodes. Only the leader node is the only direct interaction your client application has. To outside applications, the compute nodes are transparent.

Node slices

Slices are created from a compute node. A portion of the node’s memory and disc space are allotted to each slice, which uses those resources to process a portion of the workload given to the node. The workload for any queries or other database operations is divided among the slices by the leader node, which also manages data distribution to them. The operation is then finished in parallel by the slices.

The cluster’s node size affects the number of slices per node. You can choose to designate one column as the distribution key when creating a table. Rows are distributed to the node slices when the table is loaded with data using the distribution key that is specified for a table. By carefully selecting a distribution key, Amazon Redshift can load data in parallel and execute queries quickly.

Internal network

To enable private, extremely fast network communication between the leader node and compute nodes, Amazon Redshift makes use of high-bandwidth connections, close proximity, and customized communication protocols. Client applications never directly access the compute nodes because they operate on a distinct, segregated network.


One or more databases can be found in a cluster. The compute nodes house user data. Your SQL client interacts with the leader node, which works with the compute nodes to coordinate query execution.

Since Amazon Redshift is an RDBMS (relational database management system), it can work with other RDBMS applications. Amazon Redshift is optimized for high-performance analysis and reporting of very large datasets, even though it offers the same functionality as a typical RDBMS, including online transaction processing (OLTP), and functions like inserting and deleting data.

What is the AWS Redshift Pricing?

For Redshift, AWS has a very nimble pricing structure. A terabyte of data costs $0.25 per hour, and prices can increase from there. You must first select the desired node type. Three different node types are offered by AWS Redshift.

  • RA3 nodes with managed storage: The managed storage will be billed on a pay-as-you-go basis, and you will need to choose the performance level you need. The number of RA3 clusters that you must select will depend on how much data is processed each day.
  • DC2 Nodes: When you require high performance, pick these. The nodes come with built-in local SSD (Solid State Drive) storage. As the amount of data increases, more nodes will need to be added. When data is relatively small in size and demands exceptional performance, DC2 nodes are the best choice.
  • DS2 nodes: When a sizable data set needs to be stored, it should be chosen. In comparison to other nodes, DS2 only offers HDD (Hard Disk Drives) and performs less quickly. But it is also considerably cheaper. In accordance with the needs, Redshift also offers a pay-as-you-go pricing model.
  • Amazon Redshift spectrum pricing: You pay based on usage when using an S3 data lake to run SQL queries on a sizable dataset. Even if the S3 data is in the exabyte range, you will only be charged for the data that has been scanned. For the North California location, the cost was $5 per terabyte scanned.
  • Concurrency scaling pricing: Concurrency scaling permits you to dynamically allocate assets in line with the demand. AWS robotically affords extra assets even though the range of queries and customers multiply. You will simplest want to pay in line with the usage. In addition, every cluster has one scaling credit score each day. This may be enough for 97% of the clients in line with beyond AWS information.
  • Redshift managed storage pricing: This pricing version will divide the computing and garage fees of RA3 nodes. In that manner, you want now no longer upload extra nodes whilst the information requirement increases. RA3 nodes are more expensive for garage functions as compared to the use of a separate controlled garage.
  • Redshift ML.: You can use SQL queries to teach ML models. You may be capable of using the loose credit of Amazon Sagemaker earlier than you need to pay for growing ML models.

You can learn more about Pricing here.

What are the Benefits of the AWS Redshift Database?

  • Speed: The speed at which large data sets can be output is unmatched when MPP technology is used. No other cloud service provider can match AWS’s level of speed and affordability.
  • Data Encryption: For any aspect of Redshift operation, Amazon offers the option for data encryption. Which operations require encryption and which do not can be chosen by you, the user. An additional layer of security is offered by data encryption.
  • Use familiar tools: On PostgreSQL, Redshift is built. It is compatible with all SQL queries. You may also select any SQL, ETL (Extract, Transform, Load), and Business Intelligence (BI) tools that you are comfortable using. Use of the resources offered by Amazon is optional.
  • Intelligent Optimization: There are numerous ways to query data using the same parameters for a large data set. The levels of data utilisation will vary between the various commands. Tools and data are available through AWS Redshift to enhance queries. It will also offer guidance on how to automatically enhance the database. These can be used to perform an operation even more quickly and with fewer resources.
  • Automate repetitive tasks: Redshift has the capabilities that allow you to automate repetitive tasks. Administrative duties like creating daily, weekly, or monthly reports may fall under this category. Resource and cost auditing may be the answer. Cleaning up data can also be part of routine maintenance tasks. With the tools Redshift provides, you can automate all of these.
  • Concurrent Scaling: To support more concurrent workloads, AWS Redshift will scale up automatically.
  • Query Volume: In this regard, MPP technology excels. At any given time, the dataset can receive thousands of queries. Redshift won’t, however, slow down in any way. To accommodate greater demand, it will dynamically allocate processing and memory resources.
  • AWS Integration: The other AWS tools and Redshift get along well. According to your requirements and the best configuration, you can set up integrations between all the services.
  • Redshift API: The documentation for Redshift’s robust API is extensive. Using API tools, it can be used to send requests and return information. For simpler coding, the API can also be used inside a Python program.
  • Security: The security of the cloud is handled by Amazon, and users are responsible for the security of the applications that are hosted there. To add an extra layer of security, Amazon offers provisions for access control, data encryption, and virtual private cloud.
  • Machine Learning: Redshift predicts and analyses queries using machine learning. Redshift’s performance is quicker than that of competing products on the market thanks to this and MPP.
  • Easy Deployment: In a matter of minutes, a Redshift cluster can be set up anywhere in the world from any location. In a matter of minutes, you can have a high-performing data warehousing solution at a fraction of the cost set by competitors.
  • Consistent Backup: Amazon regularly performs automatic data backups. In the event of any errors, failures, or corruption, this can be used to restore. The backups are dispersed among various places. So the possibility of errors occurring at a location as a whole is eliminated.
  • AWS Analytics: AWS has many analytical tools available. Redshift can coexist peacefully with all of these. Redshift can be integrated with other analytical tools thanks to supporting from Amazon. Redshift has the ability to directly integrate with AWS analytics services.
  • Open Formats: Redshift can output data in a wide variety of open formats and supports them. Apache Parquet and Optimized Row Columnar (ORC) file formats are the most frequently used formats that are supported. The ecosystem of partners. One of the first cloud service providers is AWS. For their infrastructure, many customers rely on Amazon. AWS also has a broad network of partners who create outside applications and provide implementation services. You can also use this partner ecosystem to see if you can locate an implementation solution that is ideal for your organization.

What are the Limitations of the AWS Redshift Database?

AWS Redshift is a great offering by AWS in terms of data warehousing and processing large datasets. However, there are certain limitations that you may want to consider. Let’s have a look at some critical limitations:

  1. SQL Database – Redshift is a SQL database, and it expects the data to be in a structured format. Loading data with semi-structured and unstructured format is not easy to do.
  1. Limited Parallel Uploads – The redshift database can load the data from Amazon S3, DynamoDB, and EMR using Massive Parallel Processing. However, if you are using other sources, it doesn’t support parallel upload. It would be best if you used ETL Solution, and JDBC Connectors to upload the data from other sources.  
  1. The uniqueness of the Data – The redshift database doesn’t enforce any uniqueness to the data while loading. It would be best if you defined the keys to provide specificity to the data.
  1. No Indices – Being a Massive Parallel Processing system, the Redshift database doesn’t offer indices to the data. You need to manually create sorting keys, and define the distribution strategy, and the optimum compression setting for your table column.


In this blog post, you have gained knowledge about the AWS Redshift Database Architecture, features, and limitations. AWS Redshift provides out-of-the-box capabilities to process a huge volume of data to generate insights. However, if you’re looking for an easier solution, we recommend you to try –  Hevo Data, a No-code Data Pipeline that helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write code repeatedly. Hevo, with its strong integration with 100+ sources & BI tools, allows you to export, load, transform & enrich your data & make it analysis-ready in a jiffy.

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