Amazon Redshift Reviews

Amazon Redshift is very fast. It has really good response times. It's very user-friendly.

Redshift is a multi-tier engine that works like a calculator. There is some missing functionality and sometimes it's so difficult to work in. We need to convert these functionalities using VACUUM inside Amazon Redshift and then it causes some complexity. Sometimes I'd like for them to support some special features or some special installations because we need automatic populations. I would like to see more programming outside of the cloud. I would like to see more functionalities under JSON files. the only functionality that they have now with JSON is reports. I would also like to see other data sources like MongoDB.

I have used Amazon Redshift for three years. I use the latest version because it is on Amazon's public cloud.

The management of the dates for what we can deliver to it, it's always specific to the form that's defined to Amazon Redshift.

I have used their technical support only a few times. Before talking to support I usually try to troubleshoot things myself and I'm usually able to resolve any issues. 

In the past year, we have used Azure, but the committee has chosen Amazon Redshift because it is better than Azure for our company needs. We have grown around Amazon Redshift and other AWS solutions.

Amazon Redshift is not as straightforward as other AWS tools but it is not that difficult.

Amazon Redshift costs less than Oracle Cloud or Microsoft Azure.

I started using Amazon Redshift because I started working for this company that was working with both Azure and Amazon. The company eventually moved all to Amazon. I wasn't sure why they didn't continue to use Azure. My experience was more with Microsoft technology so I prefer Azure. But, there are some interesting features in Amazon Redshift that works better. I have also used Oracle Cloud.

I would recommend Amazon Redshift as it is part of the AWS platform and they are the biggest in the world.

I would give Amazon Redshift a rating of eight on a scale of ten.

Redshift gives extremely fast response involving large tables. This is the most important feature I look for in data warehouse solutions. Often you came across use cases where it is not possible to distribute data on a certain column, yet you need this column in join conditions. Redshift stores data using a columnar approach, which is useful for data aggregation.

All this at an extremely low price makes it possible for small to medium sized organizations to use Redshift’s power to get business insights.

One of my clients required large amounts of data but had a low budget. Amazon Redshift was the perfect choice for my client. We joined two tables containing billions of rows each and got results back in 27 seconds with a relatively small cluster of nodes.

Amazon should bring more SQL functions that are required in data warehouse implementations. It lacks SQL functions for complex data processing. A very small example is recursive queries. However, Amazon is developing the product at a fast pace and bringing new features with every release.

I’ve been using Redshift for more than two years. I created one traditional data warehouse with 3-tier architecture and one big data solution.

We have not really had stability problems. The product is mature and can be utilized for production systems.

Since Redshift is on AWS cloud, scalability is not an issue. With a few clicks, cluster size can be increased or reduced. This is useful especially when you expect a large amount of data processing temporarily. For example, on Black Friday retail organizations expect large amounts of data flow/processing. Redshift can be scaled up for few days to accommodate the surge of data and then scaled back to normal cluster size to save OPEX.

The AWS team gives special focus to customer support. This is a very big benefit of going to the cloud. You get a reply from AWS in small time frame.

I worked on Teradata and IBM solutions. Redshift gives performance similar to these solutions and costs a fraction of the amount.

Your Redshift can be up and running with few clicks and in less than 5 minutes. A big benefit when you shift to cloud.

We analyzed Microsoft, Oracle, AWS RDS and Mango DB for our requirements.

Redshift is based on PostgreSQL and adds MPP/columnar features to make it a data warehouse product. It is very easy for developers to adopt this solution. Your existing team can easily work on Redshift with no extra cost of learning.

We are using the private cloud model of this solution. Our primary use case is for a data warehouse for BI.

The most valuable features are that it's easy to set up and easy to connect the many tools that connect to it.

Compatibility with other products, for example, Microsoft and Google, is a bit difficult because each one of them wants to be isolated with their solutions. That's a big problem now.

It is stable.

Scalability is okay. It's easily scalable. We don't have any plans to increase usage at the moment. We currently have two users directly using this solution. Indirectly we have around 50 users.

We require two staff members for maintenance and others are just consuming data from it.

There hasn't been a need to contact technical support at this point. We haven't had any technical issues. 

The initial setup was straightforward. The deployment took a few hours. 

We integrated it ourselves. 

We have seen ROI. It's been useful.

It's around $200 US dollars. There are some data transfer costs but it's minimal, around $20.

I would rate it a ten out of ten. 

• Storing and querying data in near real time.
• Loading millions of raw CSV records.
• Running data comparisons and queries, then shutting it all down all within a few hours, once a week.

Easy to load and reload data.

• Fast load times 
• Flexibility in column definitions
• The ability to reload data multiple times at different times.

• It would be nice if it was a bit cheaper to retain data long-term. 
• Should be made available across zones, like other Multi-AZ solutions.

Per hour pricing is helpful to keep the costs of a pilot down, but long-term retention is expensive.

We use Amazon Redshift to store customer data. Basically, it's just to store our customer data so that we can use our own data.

I use Redshift Spectrum a lot, so that integration is good. Also, Amazon Redshift offers a relatively flexible structure, especially in terms of like, nodes that you can assign. Moreover, it is serverless, which is great.

Specifically, with Redshift Spectrum, some SQL commands have to run on Redshift instead of Redshift Spectrum, which slows down a few things in the tool. In the solution, user-based access is quite hard. In general, certain permissions are difficult to manage. The solution is expensive compared to Snowflake. I have used Snowflake because they're cheaper than Amazon Redshift. Some of the commands are run on Redshift itself, and some of the commands are completed by Spectrum, which is problematic. However, it is faster when computed by Spectrum.

I have been using Amazon Redshift for six to seven years. Also, I don't remember the version of the solution I am using. I think it's multiple different versions across the platform. So, I think it would be the latest version for some areas of our platform and older versions for other areas. My company has a partnership with Amazon.

Stability-wise, I rate the solution an eight out of ten. I can't remember what stability issues we had faced in the past, but I feel that it was due to overloading clusters.

Once we moved to a serverless version, it became more scalable. The concurrency of queries on the same cluster has a big scalability issue. Scalability-wise, I rate the solution a seven out of ten.

Around 300 people in my company use the solution.

The solution's technical support is good since we have a partnership with Amazon and we have individual support. I rate the technical support a nine out of ten.

Positive

Previously, we were using Snowflake in our company. We switched since Amazon Redshift was cheaper. Now, I use Snowflake and Amazon Redshift.

I wasn't involved in the solution's initial installation. The deployment model is largely managed by our platform teams. Also, we have a team of engineers who constantly update and maintain the solution. Probably a team of engineers is specifically focused on improving our Amazon infrastructure.

I am not sure about the pricing of the tool since that's not my department. I know that Snowflake is cheaper to set up, manage, and store data. On a scale of one to ten, where one is a low price and ten is a high price, I rate the pricing a seven.

I recommended it for data storage. I don't recommend integrated solutions, like Pacemaker unless you have an advanced team to handle it. I like using it, but it's too expensive. So, I rate the overall solution a seven out of ten.

We primarily use Amazon Redshift for prediction modeling and business reporting.

The most valuable feature is its scalability.

The technical support should be better in terms of their knowledge, and they should be more customer-friendly.

I have been using Amazon Redshift for approximately one year.

This is a stable product.

The scalability is very good, and it is one of the best features of this solution.

We have approximately 10 people who use it, four of which are engineers. At this point, we do not have plans to increase the number of users.

The initial setup is straightforward. I do not recall, however, how long it took to deploy.

In summary, this is a good solution and I recommend it. That said, the customer support needs to be improved.

I would rate this solution a seven out of ten.

The most valuable features to us are: speed, DML, the fact that it is cloud-based, the management console, and Boto3.

Because we are dealing with a lot of data, speed is always important. Redshift is blistering fast when doing "deep" copies and inserts. Conceptually, my data-transformation pipelines are a series of proprietary "waves" that leverage Redshift's DML/"deep" copy/insert strengths. Doing all this in the cloud allows us to easily test alternatives. We create different sized Redshift clusters and orchestrate them by using the SDK (Python Boto3). We go beyond the traditional DWH to "infrastructure-as-software".

Redshift has helped to transform Makerbot into a data-driven company.

Integrating database security/access rights with AWS IAM would be great. I would also like to see more DML features that might aid in processing unstructured or log-file data. This would allow us to avoid having to use EMR/Hadoop.

We’ve used Amazon Redshift for 3 years.

We did not encounter any deployment issues.

We did not encounter any issues with stability.

We did not encounter any issues with scalability.

I think the customer services is adequate.

The level of technical support is good.

We tried prior solutions, but they had limited or no scalability/agility.

The initial setup was straightforward.

It took less than a year for the product to pay for itself.

Regarding pricing and licensing, I advise to start small and have your developers/DBA use table compression and partitioning from the start.

We have used different options over the last 20 years. We found AWS Redshift to be the leader in capability and provides an ecosystem of related services from AWS, many of which are free.

My advice to other is to prototype, prototype, prototype! Everything depends on your data and what you need to do to it. No two projects are the same.

We use it to build a data warehouse and a centralized location for all of our data sources, allowing for in-depth analysis by using SQL queries.

• Allows for the storage of huge amounts of data. 
• Assists users to perform ad hoc analysis on a lot sources together.

• Windows functions, such as LEAD and LAG.
• Allows you write complex queries and perform row by row processes.

In the next release, a pivot function would be a big help. It could save a lot of time creating a query or process to handle operations.