Amazon Redshift Features

Amazon Reshift is one of its kind cloud data warehouse appliance on the AWS cloud. It is lightning-fast, Massively parallel processing (MPP), scalable data warehouse and data processing solutions without a massive investment in infrastructure. Redshift provides access to other tools like storage service S3, data lake, machine learning modules, etc. Amazon Redshift is a lot different compared to other MPP data warehouse appliances like Netezza, Greenplum, etc. In this article, we will check Amazon Redshift features, benefits and its uses.

Redshift is a great Platform as a Service (PaaS) offering for data warehousing from Amazon Web Services. It provides many features that will make your life simple when you are working on predictable and relatively small data sets and do not want spend more money on infrastructure setup.

Here are some key features of Amazon Redshift:

Redshift is a Column-oriented databases
Amazon Redshift is Secure with End-to-end data encryption
Its Architecture is Massively parallel processing (MPP)
Redshift is Cost-effective
It is Scalable
It is Easy to setup, deploy, and manage
Redshift is Fault tolerant
It provides Result caching
Includes Machine Learning to enhance performance
It can be Integrated with third-party tools
Integration with other AWS services
Redshift provides Spectrum for Semi-structured Data
Amazon Redshift Scale compute and storage independently for fast query performance
Elastic Resize

Now let us go through these features in details:

Column-oriented databases

In a database, data can be organized either into rows or columns. Many databases that support OLTP are row-orient systems. i.e. these systems are designed to perform a large number of small operations such as DELETE, UPDATE, etc.

Whereas column oriented database such as Redshift is designed for increased speed when it comes to accessing large amounts of data. Redshift is designed for OLAP operations. i.e. optimized for SELECT operations.

How Column Oriented Database Stores Data?

Secure – End-to-end data encryption

No business or organization is exempt from data privacy and security regulations, and encryption is one of the pillars of data protection. Amazon Redshift supports SSL encryption to secure data in transit and hardware-accelerated AES-256 encryption for data at rest. All data written to disk and any backup files are encrypted. You do not have to worry about key management, Amazon will take care of that for you.

Massively parallel processing (MPP)

Just like Netezza, Redshift is MPP appliance. MPP is a distributed design approach in which several processors apply a “divide and conquer” strategy to process large data sets. A large processing job is broken into smaller jobs which are then distributed among compute nodes. The compute node processors complete their computations simultaneously rather than sequentially.

Cost-effective

Amazon Redshift is the most cost-effective cloud data warehouse solution. Approximate cost estimation is one-tenth of the traditional warehouses on premise. There are no hidden charges, users only pay what they use. You can get more information on pricing on Redshift official website.

Scalable

Amazon Redshift is a petabyte scale data warehouse solution. Amazon Redshift is simple and quickly scales as your need. With a few clicks or a simple API call, you can easily change the number or type of nodes in your data warehouse, and scale up or down as your requirements.

Easy to setup, deploy, and manage

Amazon Redshift is simple to set up and operate. With few clicks you can create redshift on AWS console. You can create schema and deploy your data warehouse solutions.

In Redshift, most administrative tasks are automated, such as backups and replication, so you can focus on your development and data, not the administration.

Fault tolerant

Fault tolerance refers to the ability of a system to continue functioning even when some of its components fail. Amazon Redshift provides many features that enhance the reliability of your data warehouse cluster.

Redshift continuously monitors the health of the cluster all the time, and in case if any hard drive failure, it will automatically re-replicates data from failed drives and replaces nodes as necessary for fault tolerance.

Result caching

Amazon Redshift uses result caching to deliver sub-second response times for queries that you execute repeatedly. This feature helps to boost dashboard, visualization, and business intelligence tools performance that executes repeated queries.

Machine Learning to enhance performance

Whenever you submit the query to the Redshift, it uses sophisticated machine learning algorithms to identify the query execution time and assign the resources as and when required.

Integrated with third-party tools

You can integrate the Redshift with industry leading third party tools to perform loading, transforming and visualizing data. Redshift is working with many partners enhance the support. Redshift can easily integrate with business intelligence tools such as Tableau, QlikView, and Amazon QuickSight.

Integration with other AWS services

Amazon Redshift integrates seamlessly with other AWS services such as Amazon S3, Amazon EMR, and AWS Glue, making it easy to load and analyze data from a wide range of sources.

Redshift Spectrum for Semi-structured Data

Spectrum is a feature of Amazon Redshift that allows you to run SQL queries against data stored in Amazon S3. With Redshift Spectrum, you can use Redshift as the primary query engine for your data, while keeping your data in S3, without having to move the data into Redshift.

Redshift Spectrum enables you to take advantage of the scalability, cost-effectiveness, and durability of S3, while still being able to perform complex queries using Redshift’s advanced analytics capabilities. This enables you to use Redshift as a single source of truth for all your data, regardless of where it is stored.

In summery, Redshift Spectrum is a powerful and flexible feature that enables you to perform fast and cost-effective analysis on large amounts of data stored in S3, without having to move the data into Redshift.

Scale compute and storage independently for fast query performance

With new RA3 instances with managed storage, you can scale compute and storage independently for fast query performance. The new RA3 instances with managed storage allow you to pay per hour for the compute and separately scale data warehouse storage capacity without adding any additional compute resources and paying only for what you use.

Elastic Resize

Elastic resize in Amazon Redshift allows you to dynamically adjust the number of nodes in your cluster, either up or down, without any disruption to your applications or data. This provides you with the ability to scale your cluster resources to meet changing demand, and to optimize costs by reducing the number of nodes when demand is low.

Hope this helps 🙂