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.

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 this solution for production and customer data for one of the lines of the business, which is basically one of the warehouses. I oversee the whole architecture infrastructure.

The price isn't bad for the performance for a cloud data warehouse. It's also connected to Databricks but uses SQL. It's comparable to BigQuery. If the analyst knows SQL, which is comfortable and easy to use to go between all of these tool stacks, I think it's reliable. It's a secure and reliable data warehouse.

The performance is good, and it's pretty fast. We also have Looker and MOLE connected to it for visualization, which works seamlessly. We're storing a lot of data. There's a lot of transactional data, clickstream data, and telemetry about the customer, what they're purchasing, call logs, and marketing data.

The analysts are familiar with SQL, and they're able to do this. Even the data scientists who aren't that savvy in Python, because they are very strong in SQL, are able to interact with it very quickly. I'm able to bring in more analysts for support.

There are physically too many pipelines for a company of this size to maintain. For a data scientist, it's very difficult to learn the data in all of these different environments. It's easier to train people in just one environment to start with, like Snowflake or Databricks. It's difficult to have so many technologies that are very comparable, and each comes with a price tag.

I have used this solution for seven months in my current company, but I have also used it in a couple of my previous companies.

The stability is good. For a mid-size company, it's very stable.

It's very scalable.

I would rate the technical support as 10 out of 10.

We had one or two tickets, and they responded extremely quickly. Technical support is good, but that's because we aren't running into many issues. The solution is pretty stable. The individuals who set it up did a very good job.

It wasn't too complex because only a few people were needed to set up the solution.

It's not very pricey compared to other tools. I would rate the price as 5 out of 10.

In my organization, we also use GCP, Amazon EC2, Databricks, and Snowflake. We also have Delta Lakehouse. I'm going to move everything into the Delta Lakehouse. For a company of this size, it's a lot of tools to physically maintain with a small data engineering team.

I think Snowflake has a few more features. In Redshift, you need to write a bit more SQL in some instances, but it's very user-friendly and fast. It can be used as a data warehouse solution as well. It can also do some analytics.

Redshift is comparable to other solutions. I wanted to go with Amazon EC2 because we also have Databricks, and I think I can cover some of those features with the combination of that.

I would rate this solution as eight out of ten. It's a very strong solution.

My advice is to do your research and see if it makes sense for you. You can always request a demo from Redshift.

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.