Amazon Redshift Reviews

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.

If you want to create an enterprise data hub, that is where Redshift is used. Snowflake, Redshift, BigQuery, and Azure Synapse are enterprise data warehousing and cloud data technologies. Large enterprises have enterprise data. They have a lot of managed processes, business processes, customers, products, different assets, locations, equipment, etc. Then they have sales and marketing. There's a huge amount of data that is generated, and they will need a large warehouse or multiple data warehouses to create analytics out of that data.

We try to tell organizations to consolidate all their data into a single unified data platform that has all the enterprise data rather than being processed by multiple warehouses. It's processed on one central data platform, which is cloud-based. In which case, we recommend one of these four. We either recommend Snowflake, Azure Synapse, AWS Redshift, or Google BigQuery. It depends on what their early investment is and what kind of work they need to do.

Redshift is completely Managed on AWS cloud.

I would like to see improvement in the pricing and the simplicity of using this solution.

The product is very stable, and so are all other cloud-based managed Enterprise data platforms (Snowflake, BigQuery and Azure Synapse)

It's scalable as it's on hosted and Managed on AWS cloud.

Technical support is great, very professional.

Neutral

I would recommend Snowflake the highest, then Google BigQuery, Azure Synapse, and then Redshift.

If somebody is heavily invested into Microsoft, then going for Azure Synapse is what we recommend. If they're open to moving to a completely new system, we evaluate the landscape and we recommend either Snowflake or Google BigQuery. What we recommend and what we design and create and implement for our different enterprise customers is very different for each customer. There's no One-size-fits-all solution.

For example, for one of our customers, we have helped design and create their entire single unified data platform using Snowflake.

I would say Redshift needs a little more effort and expertise for setting up the kind of infrastructure one need. If you can do something with two-three people for Snowflake, you would need four people on Redshift. You need to have a little bit of knowledge of the AWS Cloud and AWS services to be able to use Redshift. A typical Redshift based Enterprise data work would need anywhere between 4 to 15 people.

The return on investment of moving from an on-premise to a completely cloud-hosted data platform is significantly high and worth the effort.

Redshift is costly compared to other solutions.

It's pay per use. You can have multiple models. You can go for yearly cost, which is a little discounted than the monthly cost. Depending on how much data you process and store, you can have different pricing. There's no fixed cost. All of these are based on how much data you store monthly and how much data you process.

I would rate this solution 6 out of 10. 

If an organization has invested heavily in AWS services and they have a good knowledge of the AWS ecosystem, then I would recommend Redshift. Otherwise, I would still recommend Snowflake because Snowflake works very well with AWS services. I can have my AWS S3 buckets in which I can store my enterprise data lake, and then Snowflake works with that seamlessly. If the organization has good knowledge of AWS and good knowledge of RDBMS data warehouses, then we can recommend Redshift to them.

It all depends on how much investment that organization has done in Redshift. For example, we have a customer which has a very large setup. It's a large US-based company, where they have invested heavily in AWS. They're an AWS house, so they like everything about AWS. For them, we have recommended Redshift so that the overall tech ecosystem remains optimum. 

I use the solution in my company for our data warehouse and databases.

The high price of the product is an area of concern where improvements are required.

I have been using Amazon Redshift for three years.

My company uses IBM Cognos as a reporting portal on top of Amazon Redshift.

My company moved from Netezza to Amazon Redshift since the latter is available on the cloud platform. My company used some migration tools to shift from Netezza to Amazon Redshift, and it took almost a year.

Amazon Redshift was not helpful in improving our organization's functioning, and I believe that previously, we had a better database named Netezza in place.

It is an expensive product.

The tool can handle some large-scale datasets for our company since we use data shares.

I am satisfied with the performance of the product.

The product is able to fulfill my company's needs associated with data analytics.

Amazon Redshift is used as a storage tool.

I rate the tool a seven out of ten.

We use the solution for data storage of reports.

Redshift Spectrum is the most valuable feature.

The product must become a bit more serverless. Users should have to pay only for the resources they consume.

I have been using the solution for one year.

The tool is quite stable.

Around 20 people in our organization use the product. The tool’s scalability is good.

The solution is deployed on the cloud. The initial setup was pretty easy.

The product is quite expensive.

We also tried using Athena. However, Redshift was faster.

We use the tool because we have everything on AWS. Amazon Redshift is best for fast reporting. People who want to use the solution must try using Athena. If it is not fast enough, they can try Redshift. Overall, I rate the product an eight out of ten.

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.

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.

We use Redshift Spectrum for creating temp tables during the Ignition process.

I have primarily used the Redshift Spectrum feature and found it most valuable.

During our last office project, Redshift couldn't perform well even for a data size of 6 TB. Thus, compared to Teradata and Snowflake, the solution needs to work faster. They should extend the plan by including better optimization and readability as we get while using Teradata. Also, they should provide zero-copy coding and sharing facilities.

I have been using the solution for three months.

I rate the solution's stability as a seven.

There is no issue with the solution's scalability.

The setup was straightforward as I have a POC. It was a simple process.

The solution is available at a mid-range price as compared to other vendors.

While using Redshift, we need to combine it with Glue to complete the process. Whereas, Databrix offers the same procedure without combining two solutions. Redshift would work well with small businesses if they already use AWS services. They can use Redshift if the database is not that huge. I recommend Snowflake over Redshift. I rate the performance as well as the overall product as a five.

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.