We are living in a data glut. Welcome to the Information Age, where we create 2.5 quintillion bytes (also known as exabytes) of data every day. And that’s not just from human sources—in 2020, machine-generated raw data made up 40% of online data. 

Presenting the understatement of the day: “That is a lot of data!”

Today's businesses use data to make critical decisions. Data-driven decisions are informed decisions and have a greater chance of success. In these challenging, competitive times, firms enjoy a decreased margin for error when they have the correct data to back up their strategies, which could be a difference-maker. 

But there's so much data, and a significant amount of it isn't even helpful to businesses. So how can organizations work with exabytes of data and separate the wheat from the chaff? With a colossally sized data warehouse, of course. 

That's why we're focusing today on AWS Redshift. We will answer the question: “what is AWS Redshift”, how it differs from traditional warehouses, its benefits and limitations, Redshift pricing, and setting up a Redshift database. 

But first, a small glossary.

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What’s a Terabyte and How Does It Compare With an Exabyte?

If you’re going to delve into the subject of massive data amounts, you should become familiar with these more exotic data byte measurement terms.

  • A megabyte equals one million bytes
  • A gigabyte equals 1,024 megabytes
  • A terabyte equals one trillion bytes
  • A petabyte equals one million gigabytes (roughly 1,024 terabytes)
  • An exabyte equals 1,024 petabytes

These vast numbers paint a vivid picture of how much data there is out there.

So, What Is AWS Redshift?

If you want to answer the question “what is AWS Redshift,” where better to go than the host’s website? According to Amazon AWS, AWS Redshift is “…a fully managed, petabyte-scale data warehouse service in the cloud.” So, if you’re looking for a resource to help your organization or business handle the terabytes of new data generated daily, you have come to the right place.

Amazon's warehouse product is perfect for large-scale data analysis and storage, as well as large-scale database migrations. Redshift employs massively parallel processing (MPP) technology, so it can process vast amounts of data at fantastic speeds and is very cost-effective in the process. 

Every Amazon Redshift data warehouse includes a set of nodes organized into a cluster. Each cluster runs its Redshift engine and holds at least one database. Although Redshift is an analytics database, it's flexible enough to allow cloud users to run traditional relational databases. In addition, it's a column-oriented database, so it stores data in a columnar format that boosts the performance when it reads and writes data. 

Redshift is a fully managed data warehouse, giving users the capacity, to begin with, a few gigabytes of data and eventually scale it to petabytes. It's also called an OLAP-style (Online Analytical Processing) database.

What Is AWS Redshift: AWS Redshift Database Example

This sample database is called TICKIT, and its dataset can be loaded by consulting the Amazon Redshift Getting Started Guide. The database contains seven tables, consisting of two fact tables and five dimensions. Here’s a chart showing the layout, courtesy Amazon AWS.

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Amazon Redshift vs. Traditional Data Warehouses

Now let’s see how Amazon Redshift compares to conventional data warehouses, breaking down the faceoff into four key areas.

  • Cost: The traditional data warehouse model comes with a hefty price tag. The charges start with a substantial outlay for hardware, then hiring dedicated personnel to operate the machinery. And let’s not forget maintenance costs. On the other hand, Redshift is a cost-effective, fully-managed solution that imposes no onerous startup or maintenance costs.
  • Performance: Redshift handles data processing and queries with lightning speed, thanks to its massively parallel processing (MPP) and columnar data storage configuration. So, when it comes to performance, Redshift leaves traditional data warehouses in the dust.
  • Scalability: When customers want to scale their traditional on-premise data warehouses, it means more hardware if process and storage demands increase. Redshift lets customers make changes instantly, bringing a degree of inexpensive flexibility and elasticity that traditional data warehouses can’t touch. In addition, the Amazon Redshift option offers an on-demand pricing structure (which folds into the cost advantage mentioned earlier) where customers pay only for what they use. Traditional data warehouses don’t offer anything close to this; if you need to reduce your hardware, it will take resources and work.
  • Security: Cost and security are possibly the two most significant considerations when businesses shop around for the best data storage solution. Although Amazon Redshift meticulously follows a set of security best practices, many organizations get a huge psychological boost from having their data on-premises in an ostensibly safer storage situation. However, a typical business infrastructure is just as vulnerable to attack as a cloud storage environment. Still, it's that mindset of having valuable assets close at hand that wins the day for some. 

What Is AWS Redshift and What Are the Benefits and Limitations?

Although we’ve touched upon many advantages in the Redshift vs. standard warehouse comparison, we should still spell out the pros and cons without framing them against alternate solutions.


Here’s a partial list of Redshift’s advantages.

  • AWS Integration: AWS is one of the three most popular and often-used cloud solutions (the other two being Azure and Google Cloud), and Redshift works exceptionally well with Amazon Web Services.
  • Data Encryption and Security: Amazon offers many layers of security to its clients, including access control, virtual private clouds, and voluntary data encryption; the client can decide what needs encryption.
  • Speed: Redshift offers unmatched speed, thanks to MPP technology.
  • Easily Deployed: You can deploy a Redshift cluster in minutes and for a fraction of the cost of a traditional data warehouse.
  • Regular, Consistent Backups: Amazon conducts regular, consistent backups, ready for use in restores and data recovery operations. Furthermore, Amazon stores this data across a set of locations.
  • You Can Use Familiar Tools: Redshift uses PostgreSQL, so all SQL queries work with it. Plus, you can choose any ETL (Extract, Transform, Load), SQL, and Business Intelligence (BI) tools you typically use.
  • Repetitive Task Automation: Nothing is as annoying and time-consuming as performing the same irritating little (but completely necessary) tasks every day (or week, month, or whatever).  Redshift lets you automate these pesky repetitive jobs, freeing your staff to tackle the more challenging responsibilities.
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Redshift isn’t perfect. It has its drawbacks, including:

  • Potentially Costly Migration: Organizations use Redshift because they have petabytes of information to deal with, which can be a problem if the company has bandwidth limits. Moving that volume of data to AWS cloud facilities could run into some serious money.
  • Parallel Upload Limitations: Redshift supports Amazon S3, DynamoDB, and EMR databases for parallel uploading. For other sources, you will have to use separate scripts to upload data.
  • Uniqueness Limits: Redshift doesn’t have any tools or means to ensure data uniqueness, so you may encounter redundant data points.
  • OLAP Limitations: Redshift is an OLAP database optimized for making analytical queries on huge amounts of data. However, compared to traditional OLTP (Online Transaction Processing) databases, OLAP comes up short when performing basic database tasks such as insert, update, and delete operations.

What Is AWS Redshift and How Much Does Redshift Cost?

Current 2021 prices start as low as USD 0.25 per hour for a terabyte of data, and you can scale from there. Here is the current pricing information provided by Amazon’s Redshift pricing page.

Amazon also offers a pay-as-you-go pricing structure, which conforms to your requirements. 

AWS Redshift Tutorial: How Do I Set Up Amazon Redshift?

It’s easy to set up Amazon Redshift. Just follow these simple steps!

1. Get an AWS account: If you don’t already have an Amazon Web Services account, set one up.

2. Open a firewall port: Redshift needs an open port, and it typically defaults to port 5439, so make sure that port is available in your firewall. Alternately, you can identify a different open port in your firewall when you create your cluster but be warned: you can't change the port number after creating the cluster.

3. Grant permission to access other AWS resources: You need to give Redshift permission to access other AWS resources. Either create a dedicated IAM role attached to a Redshift cluster or provide the AWS access key to an IAM user who has the required permissions.

4. Launch a Redshift cluster: Log on as the user with the necessary permissions and open the Amazon Redshift console.

5. Choose the region: Select the region where you want to create your first cluster.

6. Enter the values: Select the Quick Launch Cluster and fill in these values:

  • Node type: dc2.large.
  • Number of compute nodes: 2.
  • Cluster identifier: examplecluster.
  • Master user name: awsuser.
  • Master user password and Confirm password: Enter a password for the master user account.
  • Database port: 5439.
  • Available IAM roles: Choose myRedshiftRole.

7. Wait: Hit Launch Cluster, then wait a couple of minutes for the launch to finish. When it’s done, click Close to return to the cluster list.

8. Choose the cluster: Click the Cluster button located above the list, then click Modify. Select the VPC security groups you wish to associate with the cluster and click Modify to save your choice.

9. Authorize access: You must configure a security group to authorize access. If the cluster comes from an EC2-VPC platform, follow these steps

From this point, you can perform tasks such as running queries. Again, consult the AWS website for more detailed instructions.

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