Amazon Redshift Reviews

My primary use for Amazon Redshift is for analytical purposes.

In terms of valuable features, I like the columnar storage that Redshift provides. The storage is one of the key features that we're looking for. Also, the data updates and the latency between the data-refreshes are valuable.

Pricing is one of the concerns that I have because if you compare Snowflake with Redshift, it provides some of the same services, but at a much cheaper rate. So pricing is one of the things that it could improve. It should be more competitive.

Otherwise, everything else looks good, especially the data storage and analytical processes.

I just started using Amazon Redshift. I'm still learning about the product and about how the pricing and the dynamic skilling are done.

I understand that Amazon Redshift is quite stable and it has features like higher readability. So I think it should be a good product to be used.

I have not been in touch with support but I will be needing their support in the near future to help me set up the environment.

In terms of the initial setup, as I said I've still not started using it, so I pray that it's going to be pretty easy for me to set it up.

Of course I would advise others to choose Amazon Redshift, as long as pricing is not a concern for them.

On a scale of 1 - 10, where 1 is the worst and 10 is the best, I'd give it an 8.

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 Amazon S3 along with RedShift for storing our data. The data comes from various sources, including our client and third-parties. We get the data as an S3 file and then load it into RedShift using the ETL tools. RedShift will then act as the data source for Tableau, which is used for forecasting and other marketing activities.

The processing of data is very fast.

It would be useful to have an option where all of the data can be queried at once and then have the result shown. As it is now, when we run a query and we are looking at the results, part of the data remains to be processed at the back end. That works very well, but in some cases, we require the whole data to be queried at once and then have the results shown. We have not faced many use cases where it would have been useful, but in one or two, we used other methods to achieve this goal.

When our clients contact customer support, they don't want to speak with a machine. Instead, they want to chat with a real person who can provide a solution. Customer service bots can provide solutions but they cannot understand our problems.

I have been working with Amazon RedShift for about two and a half years.

RedShift is a stable solution.

Given that RedShift is a cloud-based solution, scalability is very good. I have not faced any issues regarding that. We have about 20 users, who are all developers.

Beyond the development stage and in terms of the users who make use of the data for Tableau, there are many people all around the world. I do not know the number, but it could be 1,000 or it could be 10,000. When it leaves us and goes into production, it is the client who takes care of it. One of the clients I am working with now has more than 10,000 personnel.

I do not have enough direct contact with the solution to see issues that would require contacting technical support. I work with the data but not so much on the technical side. It is the developers who would see these issues and would do so. The level of support is based on our subscription plan.

The company was using other solutions such as Google Cloud and the Microsoft Cloud Service, but I have not personally used these solutions.

I am very happy with what RedShift has. So far, anything that I have required has been there and whatever use case I have faced, the functionality is available.

My advice for anybody who is implementing this solution is to look into what certifications are available and which ones are required for different roles. Depending on the job, different certifications are relevant or required. For example, as a business analyst, a coding certification would not be useful for me and it would be a waste of money. These things should all be considered before beginning with any certifications.

I would rate this solution a nine 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 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.

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:

• PostgreSQL Interface
• Scalability
• Pricing/Maintenance/Setup

The PostgreSQL interface is good because you can play with big data with just SQL. This is one of the reasons why they made Hive.

However, Hive’s SQL is still not as standard as what Redshift provides:
http://docs.aws.amazon.com/red...

Redshift has been the data warehouse in at least three of my previous companies. The impact is huge to anyone who uses data in any way.

I would like to see improvements in the database integrations. Currently, Amazon does not provide real-time/near real-time integration with other products like RDS or DynamoDB out-of-the-box.

We need to either build the integrations ourselves, or rely on third-party services which are not always the best.

We have been using this solution for over three years.

There were stability issues in the beginning. However, the product has improved quite a lot in the last two years in term of stability.

Redshift can scale up to a petabyte with a few simple clicks.

Technical support is good, but similar to any other Amazon Web Service, you have to pay for a good level of technical support.

We did not have a previous solution. Redshift worked for us the first time we tried. The pricing could not be beaten by anything else in the market at that time.

The installation was straightforward and only required a few clicks.

Pricing was quite a strong point of Redshift when it was first released. Nowadays, quite a number of other services are very competitive in pricing, such as BigQuery.

Redshift, like any other big data technology, isn’t a silver bullet for everything. The most important thing is to understand your data and your requirements before you make any decision to use any technology.

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.