Amazon Redshift Reviews

We use it for data warehousing. Currently, I'm setting up a data link with Redshift to fetch data from our data lake.

The most valuable feature of Redshift is its cluster.

Redshift's serverless technology needs to improve because not everyone is technically inclined. Organizations want to quickly access and import data into their data warehouse without hassle.

Redshift's ETL tool, Glue, is not seamlessly integrated with Redshift. I've encountered many instances where it couldn't fetch the perfect data type from the source, which should be intuitive. Snowflake's ETL tool, on the other hand, is more intuitive and seamless.

I have been using this solution for two years. I am working with the latest version.

I haven't faced any stability issues because when it works, it runs continuously.

The initial setup is a complex process, especially for someone who is not familiar with nodes and configuring terms like RPUs. You need to consult the documentation to understand what an RPU is.

Moreover, Redshift can be difficult to maintain, especially the Redshift cluster instance.

When it comes to the initial deployment and implementation process of Redshift, there are two types of nodes to choose from: DC2 and RA3, which are for different requirements based on the load. One is for storage, one is for storage and checking, and one is for the computing center.

First, the user needs to know their exact requirement, unlike Snowflake, which automatically scales up and down based on the requirement using the Retrieval Service tool.

The service has not matured yet, and for the Redshift cluster, scaling has to be done manually. The cluster also needs to be set up manually, which is not ideal, especially when Snowflake is already in the market.

It is easy to deploy if you already know how to use Redshift. But if I were a new customer, I might need assistance.

Redshift is a bit less costly than Snowflake, but the effort justifies the cost for Snowflake.

I would suggest starting with a three-cluster that is DC two large, especially if you are setting up a cluster-based search. We offer a three-month or one-month trial, which will allow you to see if you can handle the manual scaling up, scaling down, and maintenance of Redshift. If not, then you can switch to a serverless data solution.

Overall, I would rate it a seven out of ten.

It is storing warehouse data for the organization. We commission data warehousing, storage of data, and reporting.

Data Science and Machine Learning are valuable features. We have found Machine Learning use cases are very nice.

Infinite storage is available in Snowflake and is not available in Redshift. Analytical tools for integration would be helpful in the future.

I have been working with Amazon Redshift for seven years now.

We have found Amazon Redshift to be stable.

We have no problems with scalability.

The initial setup is very smooth and took us about six months to deploy.  The cost is comparable to Snowflake.

I would rate Amazon Redshift a nine out of ten. I am very satisfied with it.

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.

Columnar storage technology is the most valuable feature of this solution.

We can get the SLS/SLAs in our daily processes.

Some improvements can be brought about in:

Restore table:

I would like to use this option to move data across different clusters. Right now, you can only restore a table from the same cluster.

Right now, the feature only permits bringing the table back in the same cluster, based on the snapshot taken. I would like to have a similar option to move data across different clusters, right now I have to UNLOAD from cluster A and then COPY in cluster B. I would like to use the snapshots taken to bring the data in the cluster I need.
Maybe current design cannot be used, because it is based on nodes and data distribution.

But, our real scenario is: if we lose the data and we need to recover it in other cluster, we have to do:

1) Restore table in current table with a different name

2) Unload data to s3

3) Copy data to a new cluster. When we are talking about billions of records is complex to do.

Vacuum process: The vacuum needs to be segmented. For example, after 24 hours of execution, I had to cancel the process and 0% was sorted (big table).

Vacuum process:

The vacuum needs to be segmented, example after 24 hr of execution, I had to cancel the process and 0 % was sorted (big table)"

For big tables (billions of records). if the table is 100% unsorted, the vacuum can take more than 24hrs. If we don't have this timeframe, we have to work around taking out the data to additional tables and run vacuum by batches in the main table.

Why, because If I run the vacuum directly over the main table, and I stop it after 5 hrs, 0 records will be sorted. I would like to run the vacuum over the main table, stop when I need but get vacuumed some records. Like incremental process.

I have used this solution for around three years.

We did encounter stability issues, i.e., if you are using more than 25 nodes (ds2.xlarge), the cluster is totally unstable.

I have not experienced any scalability issues.

I would rate the technical support a 9/10 for normal issues.

However, for advanced issues, I would give it a 5/10 since I had to go directly with the AWS engineers support.

Initially, we were using the Microsoft SQL solution. We decided to move over to this product due to the DWH volume and performance.

In my opinion, the setup was normal.

Based on quality of the product and its price, it is the one of the best options available in the market now.

We also looked at the Oracle solution.

You need to make sure that the space used in DWH has to be a maximum of 50% of the total space.

You must create processes to vacuum and analyze tables frequently. Also, before creating the tables, you should choose the right encoding, DISTKEY and sort keys.

We use Amazon Redshift for our data warehouse to store a lot of our data for a client.

The most valuable feature of Amazon Redshift is its ability to handle really large sets of data. In our case, the solution does a lot of things that would be difficult to do otherwise.

Amazon Redshift is a little more expensive than other products.

I have been using Amazon Redshift for four years.

Amazon Redshift is super stable, and we haven't faced any outages or other issues.

The solution's scalability has been fine regarding how much data we can load into it. Once the solution is set up, we pay for what we use. Three people are using the solution in our organization.

Users should select Amazon Redshift depending on what their needs are. Amazon has other cheaper database products, but Amazon Redshift is a really good option for users who need a lot of computation.

Overall, I rate Amazon Redshift ten out of ten.

I have used DBeaver to connect to SAP Amazon Redshift and my main use case is for SQL. I map data from a graph database into SQL.

Amazon Redshift has benefited the company by allowing us to scale. 

The solution could improve in handling more data formats and more native support for RDF.

I have been using Amazon Redshift for approximately one and a half years.

The stability of Amazon Redshift is good.

Amazon Redshift is a scalability solution.

I have not used technical support. If I need support I ask my colleagues.

The initial implementation of Amazon Redshift is simple. There was not anything difficult about getting started with the solution.

It's important to have a grasp of the environment you're working in, including a basic understanding of Amazon AWS. Additionally, when selecting an interface to work with, it's essential to choose wisely, as you'll likely be using it for an extended period of time.

I rate Amazon Redshift an eight out of ten.

We use the solution to build a data warehouse schema for a target database for analytics. We are uploading data from different transactional databases into Amazon Redshift. We use it for reporting purposes. We use the tool mainly for querying and retrieving the data for analytics.

The fast querying of a huge amount of data greatly impacts our data workflows. All the queries work pretty fast.

Amazon Redshift is a really powerful database system for reporting and data warehousing. I like the product. It works really fast with significant volumes of data. The product covers all the main functionalities required for our data security and compliance needs. It has almost everything we need. It is the main data source for our analytics functionality. We can run our models using the data stored in the database. The ease of use is fine. It is pretty easy to integrate the solution with other products and third-party solutions.

The product must provide new indexes that support special data structures or data types like TEXT.

I have no complaints about the product’s stability.

The tool is scalable. About 30 to 50 analysts use the solution in our organization. We need one or two people to administer the solution.

I haven't heard any complaints about the support team from our DevOps engineers.

My project involves analytics and data warehousing. I use Amazon Redshift. I also use AWS Glue as an ETL tool.

I will recommend the product to others for data warehousing and data analytics. However, I do not recommend the solution for small companies that do not have enough volume of data to analyze. Overall, I rate the product an eight out of ten.

We were using the solution for our data backup, but we wanted to optimize it, so we turned to AWS Glue. Amazon Redshift wasn't really great for us and wasn't working out.

Amazon Redshift was used for data storage while moving back from S3 to Amazon. However, it lagged, taking its own sweet time for data backups which also depended on the server location. Because of the aforementioned reasons, we started losing a lot of data that wasn't even real-time data. Ultimately, this affected our analytics at the end of the day. Also, we have been trying to do some work on our AI models, which emit out recommendations based on the live dataset. There were a lot of lagging issues. So, for example, sending out somewhere around 0.1 million or 100,000 emails used to take almost 12-14 working days, and this also includes the process of pulling all the data and sending them to CronJobs. Since we wanted all this work to happen in real-time, we had to get rid of the tool.

I would like Amazon Redshift to improve its performance, analytics, scalability, and stability. Other than these points, I am not aware of any other areas to address since Amazon provides a variety of independent services for their customers to choose from, and if one were to express dissatisfaction with Amazon Redshift, Amazon would likely suggest AWS Glue as an alternative. Similarly, if another issue arose, Amazon might recommend Amazon RDS. There are a lot of things they try to upsell to you, each with its own pros and cons and in different packages offering different perks. So, it all depends on your business needs and what you choose for your business. I wouldn't criticize Amazon for this because they have created packages tailored to their customer's needs, which helps to prevent customers from looking elsewhere.

I have been working with Amazon Redshift as an implementer for three years.

Stability-wise, it has a lot of issues with threats, and that is why we went for a threat shift optimization. In short, I mean to say that it is not stable at all.

If you require a highly scalable solution, I would not recommend Amazon Redshift. We currently have 12 clients using Amazon Redshift, and the scalability of the solution is terrible. In terms of scalability, I would rate this solution a three or four out of ten.

Amazon Redshift has a lot of documentation, but the setup is fine. Three years ago, the solution's deployment process took over a month or a month and a half.

Every solution has a cost and comes in different packages. Considering these factors, AWS Glue is on top. Though Amazon Redshift is good, it depends on what kind of business you're trying to do, what type of analytics you need, and how much data you have.

For Amazon Redshift, we pay around INR 60,000 annually. The cost factor also depends on the number of existing customers. In addition to the standard licensing fee paid for AWS, we incur a cloud storage cost of around a quarter million for the amount of data. We also have to bear additional costs for data security and cybersecurity, which are well taken care of by Amazon, hence the premium pricing. There are several other features and services provided by Amazon that justify the premium pricing.

Amazon Redshift is a horrible solution. I recommend my customer to use AWS Glue since while dealing globally with real-time data, which you need to make decisions, factors like how much cost and data is needed to make a decision should be considered. Apart from this, if customers are paying a huge price for the solution, then probably Amazon shouldn't mind spending on the tool. However, it may not be necessary for small businesses with only a few thousand data points. Although Azure is a better option, some clients prefer AWS, and we had to develop a solution using AWS for our client. Overall, I rate this solution a three or four out of ten.