Amazon Redshift Reviews

We stored all of the data in the S3 bucket and would like to have it stored in a data warehouse, which is why we chose this database. 

It would be very easy for us as an end-user, who would like to access the data, rather than draw it post-transformation and store it at a database level.

The TP transactions for the creation of the tables does very well.

It is quite simple to use and there are no issues with creating the tables.

The managing updates, deletes, and role-level change performance is very low. For example, while you are doing inserts, updates, deletes, and amalgamates, the performance is very, very poor.

If you want to query the database after you have a lot of terabytes of data, the load, performance-wise, is very low.

Looking at the performance of the query, querying the database, and especially with the amalgamates when it is getting updated, it is really poor.

We like this solution and have tried all of the native services; they were working quite well. The only concern about Redshift was managing the cluster, especially the EMR cluster. Our company policy was not to use EMR clusters, especially with the nodes failing. There were many instances of downtime happening. Essentially, there was too much data traffic.

The other drawback was the CDC, as we do not have any tools that can support it.

Creating the structure is easy on the DDL side, but after you create the table and you want to transform the data to store it in a database, the performance is poor.

It takes a lot of time to ingest and update the data. After you ingest the data and someone wants to fetch it in the table, it takes a lot of time performance-wise to return the results.

We have been using this solution for three months.

We are using the latest version.

There are issues with stability and it should be compared with Snowflake.

This solution is scalable. We scale up and scale down manually when we are required to, we do not have an automatic setup.

We have three or four people using this solution.

We have contacted technical support to give our opinion and recommendations or feedback and they agreed that it needs improvement.

Previously, we tried the Snowflake database, which works really well. The expectations were really good with the performance, also the DDL, DML operations on the processing of the data.

The initial setup is simple and we did not find it very complex at all.

The time it takes to deploy depends on how many tables you want to create, or how many tables will you merge the data with.

We are switching to Azure, although not because of the product or the services that we did not like. It's about AWS being competitors for logistic companies that we are working with. Also for security reasons, we do not know how secure the data is on the cloud.

If you are competitors then you don't know if the data can be accessed by your competitor, and the team can be looking at a demographic, which could impact your sales.

We have only just started using Redshift, but we are not really satisfied with it.

I would rate this solution a six out of ten.

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.

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.

I'm the head of Data Warehouse and Business Intelligence, and our company is an Amazon customer. Most of the company's data sources were on Amazon at the time the product was deployed so it was logical to use this database. The data warehouse is quite small, compressed it's maybe 160 GB. It's not like an autonomous data warehouse or Exadata, which was almost 40 terabytes. It's a simple method of achieving extraction and loading. There is no real incremental load on this. Of course in the future, with the company growing, this should be changed and we'll probably need some kind of incremental system instead of this approach.

The product is relatively easy to use because there is no indexing and no partitions. There is no referential integrity only declarative, which is okay.

From my perspective, the product could be improved by making it more flexible. There are now more flexible products on the market that allow for expandability and dynamic expansion as the market changes with regard to data warehouses. Although the product is simple to use there can be problems. If you declare some unique key in a column and then store it, the database is going to believe this is what you have and results will be distorted. It's fine if the query is simple but if it's complex or you have too many queries per hour, it can create a bottleneck for Redshift and then you can't return and recover. It requires some fine-tuning. 

For additional features, I would like to see support for partitions, it doesn't exist yet as a feature. It's quite an important issue when you're dealing with large databases. Also, I believe the product needs improvement in parallel threading to support more database users without jeopardizing performance.

I've been using Redshift for about a year and a half. 

We sometimes have issues with stability, especially over the weekends. It performs, of course, but sometimes there are problems. 

In terms of pricing, if you plan to use the product for a small company and you compare the cost to a product like Oracle Autonomous Data Warehouse, then you need to consider potential company growth and whether you may need to expand the system in the future, require partitions, etc. It could be that in the end, Autonomous Data Warehouse is cheaper than Amazon because if you use that product it can fit your processes and run dynamically. This is not possible on Redshift. If you have two different types of servers on Redshift then at some point you'll reach a limit and will need to move to another type of cluster which is not such an easy operation. On the other hand, with Autonomous Data Warehouse, you can add extra terabytes, but that's it. Nothing else. 

I would rate it a five out of ten. 

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.

We primarily use the solution for analytics.

The solution's flexibility is its most valuable feature. It's also easy to scale and has relatively painless pricing. 

The speed of the solution and its portability needs improvement.

The solution is getting more stable with each new version. It's stable now, but it can always continue to improve.

The solution can scale easily.

I've never been in touch with technical support.

The initial setup was straightforward.

We use public and hybrid cloud deployment models. We are Amazon partners.

The solution itself is very popular. Many people use it these days.

I recently went to an AWS summit in Zurich. I was very impressed by AWS and the presentation. Their solutions are very good.

I'd rate this solution eight out of ten.

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. 

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.

• 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.

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.