Amazon Redshift Reviews

Our primary use case of this product is as a data warehouse. We are partners with Amazon. 

Amazon Redshift has a very user-friendly SQL editor and it's very easy to use the connectors. The product provides a lot of options to connect to different sources and the modeling capabilities are also good. As a fully managed data warehouse solution, the two important aspects are the connectors and how well we can use SQL for data modeling. In both cases, this is an efficient product.

Improvement could be made in the area of streaming data. The capability can definitely be improved. There are other products like Kinesis which is a separate service we use for streaming data ingestion. Whatever features are missing in Redshift, they have separate sources but if there were the feasibility to ingest real-time streaming data directly into RedShift, that would be very useful. 

I've been using this solution for the past year. 

The product is 100% stable.

The solution is scalable. This is backend technology and end users are not aware they are using it. We probably have four or five users in the company. 

The technical support is good, they're very responsive. 

The initial setup is very simple because it's a fully managed service and not much is required from the user's end.

The licensing is paid on a monthly basis, and I think the pricing is acceptable. They also have a pay-per-go model which is on a per user basis. 

If a company is already using Amazon Cloud or they're looking for a cloud solution, Amazon is one of the oldest and most reliable cloud service providers and Redshift is a no-brainer for data warehouse needs.

I rate this product a nine out of 10. 

The features I find valuable in Redshift are JSON format support. You can copy JSON to the column and have it analyzed using simple functions. Second, is the parallel off/on where you can choose if you want it to unload to split files or into one file.

Since we have lots of data sources and high volumes, we needed a unified and organized DB that can handle these amounts and will be our single source of truth for the organization. Therefore, Redshift is the best solution.

It lacks a few features which can be very useful, such as stored procedures, Also, one needs to perform Vacuum in order to manage this DB. It would be nice not to worry about that and have this manageable.

Three years.

Yes. Sometimes, for some reason, Redshift is down (not due to maintenance).

No, cause we know how to use Redshift. We have a cluster of both HDD and SSD for which we keep the maximum data in each, so it would be scalable.

Great. They are available and very helpful.

Initial setup is very straightforward, very easy. No need of any side help.

If you want to think of every query you make but want to know that your nodes are fully managed, then use BigQuery Data Analytics. If you want a fixed price, an to not worry about every query, but you need to manage your nodes personally, use Redshift.

I did not. we did consider using BigQuery Data Analytics, but eventually, we decided to use Redshift.

My rating would be 8.5. This a great product, but one still needs to know how to manage clusters and nodes.

In order to make your DB scalable and reliable. it has the greatest benefit of build on PostgreSQL, so any data specialist that has SQL experience can handle Redshift.

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.

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 COPY command, because much of my work involved helping customers migrate large amounts of data into Redshift.

It helped my customers migrate off on-premise platforms such as Teradata to Redshift, at a fraction of the cost.

There are challenges with dealing with character set mismatches. Migrating data from other data sources can be challenging when you are working with multibyte character sets.

Two years.

No.

I personally haven’t hit scalability issues but at dinner a year ago with a few of my existing customers (all Fortune 500 companies), I was told there are scalability issues once you get to 32-nodes.

One of my previous customers told me they were migrating off Redshift because they hit the ceiling and had scalability issues. They told me the responsiveness they were getting was inferior to alternative solutions once your Redshift gets to a specific size.

I never utilized AWS technical support.

I’ve helped customers migrate off Teradata, SQL Server , Oracle Exadata, Greenplum, and ParAccel Matrix to Redshift. Some due to cost savings, others because of the EOL of the product.

Setup of Redshift infrastructure is pretty straightforward. I’ve been told that setting up partitions can be tricky in order to ensure good performance.

I have nothing to add here as I wasn’t involved in this part of the process. However, one of my customers went with Google Big Query over Redshift because it was significantly cheaper for their project.

I only provided advice to my customers, but some looked at Azure SQL DW , Greenplum, Netezza, and Google Big Query as possible alternatives

Be careful with vendor lock-in! You cannot move your Redshift environment to a different cloud provider or to an on-premise solution.

Column store and distributed processing is optimized for read access. We grew to 3000+ users with no impact.

Column store is a data compression technique for relational data. I’m using it now in SQL Server 2016. We configured a 16-core VM for handling requests on the DB. The recommendation was to separate inbound data packets into related chunks, which were 1/16th of the size.

This way, the import process could make full use of parallelization, and it worked. We imported 20 million rows of sales facts in less than 15 seconds, and the content was query-able immediately. I’ve never seen that before. This was impressive. This meant that we could completely rebuild the data warehouse to “current” from "scratch" within minutes, assuming that the data was in S3 already.

Tables that would typically be 2GB in size are now about 250MB. This means more data in memory. You can also configure the tables to live in the memory of all of the available cores. This is good for small dimension tables. You can also fragment them across all cores, for the larger fact tables. This allows for distributed query processing. Once you set it up, it just worked. It was all specified in the PG-SQL table statements.

There were two data centers in Sydney that were guaranteeing us a distributed solution. We really didn’t notice this. It was more of a check box situation. At one point, there was an outage at AWS, but it didn’t impact our operations directly.

This has given us the ability to provide metrics to the large number of company staff on their performance without impacting core systems.

I’d like to see these RedShift features arrive in other languages, such as SQL's ColumnStore index.

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I have used this solution for three years.

There have been no stability issues.

Technical support always met my expectations.

I was on a team that was using AWS tools for Dick Smith Electronics (now liquidated). The tools ceased use in February of 2016.

Prior to that, we were using them fully for about 3 years. We loaded data to Redshift according to the best practices included in the online docs and through consultation with the AWS staff. The combination of S3 and Redshift for this purpose was very high in performance. Redshift was used to provide the data model to an instance of MicroStrategy for BI reporting.

We were using MicroStrategy, which generated all the SQL that our reporting services needed.

As such, I could only comment on the data engineering phase. Technically, this was so impressive that I don’t know what to add. I don’t recall feeling that it missed anything. If anything, I was not using all the available features. AWS documentation is great in this regard. You can tell they have put a lot of thought into it.

A lot of the future direction in database technology has to do with memory optimization and concurrency (VoltDB). This is more targeted towards transactional processing, and not data warehousing.

Memory-only data warehousing solves a lot of access issues without having to think too hard about the problem from the consumers' point of view. I am sure that you can already configure this.

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.