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 Amazon Redshift services to query the data and to perform certain data science operations on that data, such as applying a machine learning algorithm or doing an analysis.

The most valuable features of Amazon Redshift are that its fast and efficient. We have lots of TBs of data and it's very fast.

Amazon Redshift could improve the user interface support.

I have been using Amazon Redshift for approximately one year.

Amazon Redshift is a stable solution. However, there are many times the environment configuration changes very quickly without any intimidation and it creates a lot of problems for running our codes.

The scalability of Amazon Redshift is good. The solution is best suited for larger-scale businesses because the price is affordable for them and they need the complexity.

The support from Amazon Redshift is very good.

Amazon Redshift is somewhat complex to deploy. The process could improve.

Amazon Redshift is an expensive solution. Larger organizations can afford this solution, but smaller businesses would struggle to afford it.

I rate Amazon Redshift an eight 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.

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.

The valuable features are performance, data compression, and scalability.

Query compilation time needs a lot of improvement for cases where you are generating queries dynamically. Also, it would help tremendously to have some more user-friendly, query optimization helper tools.

We have been using the solution for 24 months now.

We have not faced any stability related issues so far.

The time it takes to scale the cluster up or down is not trivial and it can take a while. In case you need to do this fast, you will need to think about other solutions.

Apart from the official documentation, we haven't had the need to reach out to technical support yet. The quality of the documentation is very good. There are a lot of very useful articles from the community.

Previously, we were using AWS RDS for our use case. We found that we had outgrown it. Our data grew in size and we wanted to still have performance queries.

The initial setup of the cluster was pretty straightforward. The following step, setting the right table configuration, was not so straightforward, though. It required an understanding of how the product works. Sort and distribution keys are required concepts to know about.

Redshift is very cost effective for a cloud based solution if you need to scale it a lot. For smaller data sizes, I would think about using other products.

We were thinking about using a self-managed PostgreSQL. We chose Redshift because we didn't need to manage it ourselves and because it integrates with the rest of the AWS services more fluently.

We are currently evaluating Druid.

It is very important to understand how Redshift is designed to work. The database schema design is not trivial and requires an in-depth knowledge about it, especially if your use-case requires it to perform well.

The primary use case for this solution is related to statistical data.

The best part about this solution is the cost.

The performance of this solution is good if you have only a few use cases and a few queries, but for larger applications, it is not so good. 

Running parallel queries results in poor performance and this needs to be improved.

This solution is stable, although we have had some issues with maintenance.

The scalability of this solution needs to be improved.

We have dealt with technical support and it is ok.

The initial setup of this solution is straightforward.

The suitability of this solution depends on the environment and the requirements. For some, Amazon Redshift is perfect. However, some people will need better queries. Based on my research, many of the products are pretty close. It is possible that the much higher priced solutions have more differences.

This is a good solution but the queries need to perform better.

I would rate this solution a seven out of ten.