Amazon Redshift Reviews

There are many use cases, as I've worked with Amazon Redshift at different companies. Initially, we used it as part of the AWS suite, which made it easy to get started. We were able to move data from MySQL and PostgreSQL into Amazon Redshift, and we even used it in a production environment. However, the scalability of Amazon Redshift was not enough for our needs, so we switched to Snowflake.

The most valuable feature is its seamless integration within the AWS ecosystem. If your data is already in S3, it becomes easier to port it into Redshift and work with it. 

Redshift allows you to transform different data formats and consolidate them into one Redshift cluster. This means you can transform various siloed data sources like Excel files and CSV files into Redshift. 

With Redshift, you get a suite of applications that enable you to drop a file and consume it into the data warehouse, facilitating the use of Power BI dashboards, Tableau dashboards, or custom dashboards on top of Redshift data. This setup process is much smoother compared to other tools that may require going through legal MSA (Master Services Agreement) and other complex procedures.

As our scalability requirements and data growth exceeded expectations, Redshift didn't scale up to meet our business needs. So, at that point, we made a switch to Snowflake, which provided the scalability we needed. So, scalability is one area of improvement.

One area where Amazon Redshift could improve is in adopting the compute-separate, data-separate architecture, which Delta, Snowflake are adopting, and a few others in the cloud data warehouse spectrum. Although Redshift introduces Aqua to achieve some level of scalability, I still feel that when it comes to scaling up, whether it's vertical scaling or horizontal scaling, there is a noticeable amount of downtime for end consumers. So, if I need to switch from DC1 to DC2, or from one compute/storage optimization to another, I have to bring down the entire cluster and then bring it back up. That's a pain point.

I have been using Amazon Redshift since 2012.

In terms of stability, I would say Redshift's workload management is generally stable. However, the way Redshift distributes the workload is not as scalable. I often don't have clear visibility into what queries are on hold, their priorities, or other relevant information. 

On the other hand, Snowflake provides more transparent insights into the queue and running queries, and it also informs me when the queue will be drained. Snowflake's multi-cluster approach to handling workloads surpasses Redshift in these aspects.

I would rate scalability a seven out of ten. There is a need for improvement in terms of scalability. 

I did contact customer support regarding a data breach. In terms of handling such incidents, I found their customer support to be excellent. They were proactive in ensuring that our account and data were not compromised. They guided us through the necessary steps to recover and resume operations. 

The initial setup is seamless. You just need an AWS account for the setup. I would rate my experience with the initial setup a nine out of ten, where one being difficult and ten being difficult to setup. 

If I have to scale maintenance, maintenance is much easier in Amazon Redshift, provided you have data governance in place and constantly monitor the usage and things of that nature. The roles and the RBAC are not so convenient because everything goes to IAM. Whereas Snowflake has flexibility in setting up RBAC, which is entirely different than what Amazon Redshift has to offer.

If it fits the bill and the business purpose, I would recommend using the solution. But when it comes to business needs, that's when all these things matter. Whether one tool versus the other supports all the business needs and meets the SLA and RT, or whatnot, then we are talking about different products. So it's based on business needs. 

Overall, I would rate Amazon Redshift a six out of ten. They still need to adapt the maturity and be on par with the open source community and ecosystem. Recent technology adoption is towards Delta. Microsoft is moving towards Delta. A lot of ecosystems are moving towards Delta, but Amazon Redshift is not making its move towards this technology, which is serving the engine. So that aspect has to grow.  So, there are a lot of aspects where they can improve.

Redshift is an AWS warehouse solution. We have structured datasets, and we don't load all the amplitude data into Redshift. We first do this via Hudl, a data integration solution partner, but then later, it's directly loaded by an interaction. Then we run DBT against Redshift. We have our data models in DBT, and we run data analytics threats against the data warehouse.

Service accounts are used in both Amazon Redshift and Google Cloud. For example, I could create a service account for my desktop to access Redshift or a service account for multiple users to access Redshift. In BigQuery, creating a service account is very simple, and you get full control over the access, so you can limit what the service account can do. This prevents accidental exposure of data or deletion of data. Only certain features are available, which is very handy.

Postgres syntax requires 25 synthetic scrubs to Postgresify. It's handy, but there are no blockers when using the query. It's more competitive, but the price is very reasonable. I was always aware of what I would pay, and if I reserved servers, I knew what it would cost. There is no alternative in choosing a solution. We had to use the server version of AWS, but it had limited features. A few features were lacking, which couldn't front Redshift against it or access it from the API. We had our nodes, which were sent from Amazon. It has a minimal setup, with two services running only. 

It was predictable because the performance was good. When a complex BBT model was running, we reached its limits. If there was a one-node setup, not all the storage was available on the server. For example, in a machine with 72 gigabytes of storage, only four were available in a single setup. I had another node, with 64Gb. All the storage of the two servers was available and when you are running these complex queries, it's not only a bit of computing but also temporarily eats up the storage. I couldn't use a single server because temporary tables ate up the storage. BigQuery’s authentication is straightforward. Besides that, it's doing what it's expected to do. There are no major problems.

It would be good to see Redshift as a serverless offering. The proposition may be unclear, but at the time, there were certain limitations with the pay-as-you-go offering. However, a serverless offering would be more flexible on-demand pricing, which would be good to see because Redshift is not expensive, but I always have to buy a new server if I need more computing than I have. Setting up a new server is an easy task, but it would be better if I could scale my Redshift cluster up or down as needed; still, there is a need for manual control. For example, my analyst team is working on a job that requires a lot of computing and is only needed for this month, week, or even today. The job should scale up and down automatically, but it is not yet fully developed.

I have been using Amazon Redshift for one and a half years.

We've had some cases where queries would get stuck, and we'd be on them for ages. I don't have the transparency to see what other queries are already running or if we're running out of some kind of resource. There weren't many major problems, but sometimes we'd get these annoying issues, especially when running complex queries.

If we can immediately set up new servers, it's easy to do, but an automatic solution or a threshold would be ideal. This feature may be already available, but I'm not sure. We have three users using this solution. I rate the solution’s scalability a seven out of ten.

Amazon Redshift support is not always available, so it can be challenging to reach them. You have to buy time and schedule with them. There is no real need for a technical hub, but it is not there when there is a need.

The initial setup wasn't very complex.

The solution has very competitive pricing. It can be expensive for the first time when you are building your site. Time and the amount of data also take some time to downsize. It would be cheaper than to have a server, but for Plexigos storage, you have to buy a specific size of compute power. Initially, it was more expensive than BigQuery pay-as-you-go, but it got cheaper later. The more data you have, the relative ratio becomes cheaper. It depends on the use case. In AWS, you must invest and understand the setups, such as what kind of servers you need. Then, you can set up your own, which can be very cheap. Redshift can be engineering-focused to set up, which is not ideal. Azure and Google Cloud, are more efficient for data analysts who are not data engineers. But it can be effective once you get used to it and set up a process. If you are utilizing the on-demand stuff, Redshift is the only vendor offering a dedicated service.

From time to time, the solution needs to be restarted for maintenance. I recommend BigQuery over Amazon Redshift. I don't have experience with Snowflake, but it's set to be more feature-rich than BigQuery or HSA. I was more happy using BigQuery. Redshift is doing what it's expected to do, but you had to invest in learning the setup. Overall, I rate the solution a seven out of ten.

We use Amazon Redshift in our business intelligence ecosystem. It's simple to configure, cost-effective, and close to our data sources.

The managed service is scalable and reliable. AWS takes away scalability and reliability components, making it relatively easier for us.

Its simplicity in configuration, cost-effectiveness due to being in the cloud and close to our data sources, and the fact that it's a managed service that is scalable and reliable are highly valuable.

There are no significant issues preventing us from doing our tasks. However, there might be some limitations from a business intelligence perspective, but nothing we can't find a workaround for.

We have been using it for five years or more.

We are happy with it, so there are no major stability issues that stand out.

AWS handles scalability and reliability, making it easier for us to manage.

We have two people to continue with support.

Positive

Setting it up was straightforward due to its simplicity and being a managed service.

AWS handles the scalability and reliability components, making it easier to implement.

Ensure that information about specific configurations and internal uses remains anonymous.

I'd rate the solution seven out of ten.

I mostly use Amazon Redshift for data warehouse purposes. I have used it as the BI tool source and for making data transformations and keeping them stored permanently. These have been one of the primary use cases most of the time.

Amazon Redshift responds quite fast when you properly configure the cluster and the data schemas and table structures, which is very valuable.

With Amazon Redshift, the time to process a huge amount of data is very fast when you properly configure the cluster, data schemas, and table structures. The solution's speed, stability, and user concurrency have been very good.

Actually, there have been many improvements with the query editor (version two) and the serverless type of cloud cluster, which is great. The only minor issue I faced was that it took a bit longer than expected to change the cluster to have more space or storage. Otherwise, everything is great.

I have been using Amazon Redshift since 2016. Although it has not been constant in all the projects, the first time I used it was in 2016.

I have never had any issues with the stability of Amazon Redshift. It has been very, very stable.

You can configure and scale it up when necessary. However, when I had to do it, it took a bit longer than expected. Overall, I would rate the scalability of Amazon Redshift a nine out of ten.

You can have the wizard, and you can start creating the cluster. You will have it running in minutes. From that point, you can start plugging into it and serving it as a source for the BI tool.

You can start small with a basic cluster to learn and practice with it. Selecting the most basic and economical cluster type can save you enough money to move forward with the solution or go with a solution in distribution for deployment.

I'd rate the solution ten out of ten.

I have used it for our reporting solution requirement. We gathered data from different processes and applications, like the high system process. Clients can review the data; we use it for connections and reports. Additionally, Redshift generates some configuration files without using an application.

For reporting purposes, Redshift is a great tool to use. Redshift has an advantage when it comes to administration, making it easier to manage and collaborate. Additionally, its server architecture allows for faster processing. Redshift also supports prepaid costs, which is another great feature. However, similar features are also available in Azure.

Redshift has some advantages in terms of administration and performance.

When compared to Snowflake, Amazon Redshift does not have the capability to dynamically increase the VM file. However, Amazon Redshift provides a virtual database called 'VW' that allows you to increase the size of the warehouse to run faster on a monthly basis without changing anything. This feature is not available in Redshift. So it's a limitation of Redshift.

It's not possible to immediately increase the virtual warehouse size in Amazon Redshift. When compared to Snowflake, we cannot increase the virtual warehouse size in Redshift. 

I have been using this solution since 2015. 

I don't see any issues with data loss or any other problems. Although there might be some loss in the data center, we monitor it, and everything is enabled. In such scenarios, the turn-up time is much faster. We've been using it since 2018, and I've got the same product for another customer using a limited rate. So, I don't see any significant impact, and it's a very stable product.

We have to consider the scalability of this solution carefully. In production, we have a proper size. We allocate 40% for data storage and 60% for temporary segments. We cannot increase data storage usage beyond 50%. It cannot exceed 80% of the total utilization, including network speed and query performance. We monitor all of these carefully.

So if the CPU utilization goes beyond 80%, I recommend upgrading to multiple nodes. It ensures that there won't be any issues. Around 30 people are using AWS and Azure modules along with me.

We have contacted the Redshift team for support related to other installations, such as WDL configuration for project implementation for a web application. When it was not working as expected, we had to provide authentication for the web chat. So, we used to contact them for that kind of knowledge.

The initial setup depends on who is doing it. In my opinion, it doesn't require much knowledge. Since we've been using it for a long time, it's much faster for me, but it might not be the same for others.

It's on the expensive side.

One reason we chose this solution is that we are in the process of moving everything to the cloud. But that's not the only reason. My company wanted to consolidate everything into one system, and AWS provided all the necessary information in one place. For example, Lambda is for specific storage and limited membership; all this information is available in one cloud network. This way, data segregation is much faster and easier to use. It's just everything in one cloud network, so we decided to use it.

I would recommend it. However, I think we need to consider other configuration levels. You need to decide, and I would not go with the first option.

To evaluate the data you are planning to migrate, we need to assess the environment. What is the value of your data, and what type of data is it? The density of the data is also important. Before implementing Redshift, we need to ensure that the AWS configuration is activated. After that, you need to set up enrollment and increase your storage. I don't recommend making a purchase on the same day, but it is a critical moment at a high level.

I suggest purchasing a renewal that meets the deposit requirements so that you can have a good experience and optimal performance. You can increase the budget for the building process. If you have the right team, such as those with experience in AWS or those who are learning about Azure databases, they can start using Redshift without any issues.

Overall, I would rate the solution a nine out of ten. 

We were using the solution for our data backup, but we wanted to optimize it, so we turned to AWS Glue. Amazon Redshift wasn't really great for us and wasn't working out.

Amazon Redshift was used for data storage while moving back from S3 to Amazon. However, it lagged, taking its own sweet time for data backups which also depended on the server location. Because of the aforementioned reasons, we started losing a lot of data that wasn't even real-time data. Ultimately, this affected our analytics at the end of the day. Also, we have been trying to do some work on our AI models, which emit out recommendations based on the live dataset. There were a lot of lagging issues. So, for example, sending out somewhere around 0.1 million or 100,000 emails used to take almost 12-14 working days, and this also includes the process of pulling all the data and sending them to CronJobs. Since we wanted all this work to happen in real-time, we had to get rid of the tool.

I would like Amazon Redshift to improve its performance, analytics, scalability, and stability. Other than these points, I am not aware of any other areas to address since Amazon provides a variety of independent services for their customers to choose from, and if one were to express dissatisfaction with Amazon Redshift, Amazon would likely suggest AWS Glue as an alternative. Similarly, if another issue arose, Amazon might recommend Amazon RDS. There are a lot of things they try to upsell to you, each with its own pros and cons and in different packages offering different perks. So, it all depends on your business needs and what you choose for your business. I wouldn't criticize Amazon for this because they have created packages tailored to their customer's needs, which helps to prevent customers from looking elsewhere.

I have been working with Amazon Redshift as an implementer for three years.

Stability-wise, it has a lot of issues with threats, and that is why we went for a threat shift optimization. In short, I mean to say that it is not stable at all.

If you require a highly scalable solution, I would not recommend Amazon Redshift. We currently have 12 clients using Amazon Redshift, and the scalability of the solution is terrible. In terms of scalability, I would rate this solution a three or four out of ten.

Amazon Redshift has a lot of documentation, but the setup is fine. Three years ago, the solution's deployment process took over a month or a month and a half.

Every solution has a cost and comes in different packages. Considering these factors, AWS Glue is on top. Though Amazon Redshift is good, it depends on what kind of business you're trying to do, what type of analytics you need, and how much data you have.

For Amazon Redshift, we pay around INR 60,000 annually. The cost factor also depends on the number of existing customers. In addition to the standard licensing fee paid for AWS, we incur a cloud storage cost of around a quarter million for the amount of data. We also have to bear additional costs for data security and cybersecurity, which are well taken care of by Amazon, hence the premium pricing. There are several other features and services provided by Amazon that justify the premium pricing.

Amazon Redshift is a horrible solution. I recommend my customer to use AWS Glue since while dealing globally with real-time data, which you need to make decisions, factors like how much cost and data is needed to make a decision should be considered. Apart from this, if customers are paying a huge price for the solution, then probably Amazon shouldn't mind spending on the tool. However, it may not be necessary for small businesses with only a few thousand data points. Although Azure is a better option, some clients prefer AWS, and we had to develop a solution using AWS for our client. Overall, I rate this solution a three or four out of ten.

We use the solution to build a data warehouse schema for a target database for analytics. We are uploading data from different transactional databases into Amazon Redshift. We use it for reporting purposes. We use the tool mainly for querying and retrieving the data for analytics.

The fast querying of a huge amount of data greatly impacts our data workflows. All the queries work pretty fast.

Amazon Redshift is a really powerful database system for reporting and data warehousing. I like the product. It works really fast with significant volumes of data. The product covers all the main functionalities required for our data security and compliance needs. It has almost everything we need. It is the main data source for our analytics functionality. We can run our models using the data stored in the database. The ease of use is fine. It is pretty easy to integrate the solution with other products and third-party solutions.

The product must provide new indexes that support special data structures or data types like TEXT.

I have no complaints about the product’s stability.

The tool is scalable. About 30 to 50 analysts use the solution in our organization. We need one or two people to administer the solution.

I haven't heard any complaints about the support team from our DevOps engineers.

My project involves analytics and data warehousing. I use Amazon Redshift. I also use AWS Glue as an ETL tool.

I will recommend the product to others for data warehousing and data analytics. However, I do not recommend the solution for small companies that do not have enough volume of data to analyze. Overall, I rate the product an eight out of ten.

The solution can be used as a warehouse. We dump any data that exists in our company into it. We spring up different databases based on the requirements.

The tool is the one place where the company’s data is stored. We use it extensively. Previously, we had data in multiple databases and structures. Since we started using Redshift, everything is in one place. We have multiple supporting layers and dashboards connected directly to Redshift. It helps and supports all the different businesses.

It is very easy to dump data into the tool. We do not worry about partitions. It has auto-vacuum features. We need not worry about I/O speed. It is easy to integrate the solution with other AWS services like Blu and Pacemaker.

Sometimes, it's difficult to get the metadata from Redshift. The product has too many layers to get simple information. Redshift does not have primary-key tools. The vendor must consider adding them.

I have been using the solution for two years.

The stability depends on the number of clusters and the query performance. If the cluster is scaled enough, Redshift queries are pretty quick. I rate the tool’s stability an eight out of ten.

We have multiple issues with the product’s scalability. The AWS team has helped us scale to the correct amount. We do not use Auto Scaling. We use AWS experts to help us scale based on our needs.

The setup is pretty easy. I rate the ease of setup a ten out of ten.

I will recommend the solution to others. It is pretty easy to use. If we're using AWS, it is easy to use other AWS features. Overall, I rate the product an eight out of ten.