Amazon Redshift Reviews

Redshift is an AWS warehouse solution. We have structured datasets, and we don't load all the amplitude data into Redshift. We first do this via Hudl, a data integration solution partner, but then later, it's directly loaded by an interaction. Then we run DBT against Redshift. We have our data models in DBT, and we run data analytics threats against the data warehouse.

Service accounts are used in both Amazon Redshift and Google Cloud. For example, I could create a service account for my desktop to access Redshift or a service account for multiple users to access Redshift. In BigQuery, creating a service account is very simple, and you get full control over the access, so you can limit what the service account can do. This prevents accidental exposure of data or deletion of data. Only certain features are available, which is very handy.

Postgres syntax requires 25 synthetic scrubs to Postgresify. It's handy, but there are no blockers when using the query. It's more competitive, but the price is very reasonable. I was always aware of what I would pay, and if I reserved servers, I knew what it would cost. There is no alternative in choosing a solution. We had to use the server version of AWS, but it had limited features. A few features were lacking, which couldn't front Redshift against it or access it from the API. We had our nodes, which were sent from Amazon. It has a minimal setup, with two services running only. 

It was predictable because the performance was good. When a complex BBT model was running, we reached its limits. If there was a one-node setup, not all the storage was available on the server. For example, in a machine with 72 gigabytes of storage, only four were available in a single setup. I had another node, with 64Gb. All the storage of the two servers was available and when you are running these complex queries, it's not only a bit of computing but also temporarily eats up the storage. I couldn't use a single server because temporary tables ate up the storage. BigQuery’s authentication is straightforward. Besides that, it's doing what it's expected to do. There are no major problems.

It would be good to see Redshift as a serverless offering. The proposition may be unclear, but at the time, there were certain limitations with the pay-as-you-go offering. However, a serverless offering would be more flexible on-demand pricing, which would be good to see because Redshift is not expensive, but I always have to buy a new server if I need more computing than I have. Setting up a new server is an easy task, but it would be better if I could scale my Redshift cluster up or down as needed; still, there is a need for manual control. For example, my analyst team is working on a job that requires a lot of computing and is only needed for this month, week, or even today. The job should scale up and down automatically, but it is not yet fully developed.

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

We've had some cases where queries would get stuck, and we'd be on them for ages. I don't have the transparency to see what other queries are already running or if we're running out of some kind of resource. There weren't many major problems, but sometimes we'd get these annoying issues, especially when running complex queries.

If we can immediately set up new servers, it's easy to do, but an automatic solution or a threshold would be ideal. This feature may be already available, but I'm not sure. We have three users using this solution. I rate the solution’s scalability a seven out of ten.

Amazon Redshift support is not always available, so it can be challenging to reach them. You have to buy time and schedule with them. There is no real need for a technical hub, but it is not there when there is a need.

The initial setup wasn't very complex.

The solution has very competitive pricing. It can be expensive for the first time when you are building your site. Time and the amount of data also take some time to downsize. It would be cheaper than to have a server, but for Plexigos storage, you have to buy a specific size of compute power. Initially, it was more expensive than BigQuery pay-as-you-go, but it got cheaper later. The more data you have, the relative ratio becomes cheaper. It depends on the use case. In AWS, you must invest and understand the setups, such as what kind of servers you need. Then, you can set up your own, which can be very cheap. Redshift can be engineering-focused to set up, which is not ideal. Azure and Google Cloud, are more efficient for data analysts who are not data engineers. But it can be effective once you get used to it and set up a process. If you are utilizing the on-demand stuff, Redshift is the only vendor offering a dedicated service.

From time to time, the solution needs to be restarted for maintenance. I recommend BigQuery over Amazon Redshift. I don't have experience with Snowflake, but it's set to be more feature-rich than BigQuery or HSA. I was more happy using BigQuery. Redshift is doing what it's expected to do, but you had to invest in learning the setup. Overall, I rate the solution a seven out of ten.

I mostly use Amazon Redshift for data warehouse purposes. I have used it as the BI tool source and for making data transformations and keeping them stored permanently. These have been one of the primary use cases most of the time.

Amazon Redshift responds quite fast when you properly configure the cluster and the data schemas and table structures, which is very valuable.

With Amazon Redshift, the time to process a huge amount of data is very fast when you properly configure the cluster, data schemas, and table structures. The solution's speed, stability, and user concurrency have been very good.

Actually, there have been many improvements with the query editor (version two) and the serverless type of cloud cluster, which is great. The only minor issue I faced was that it took a bit longer than expected to change the cluster to have more space or storage. Otherwise, everything is great.

I have been using Amazon Redshift since 2016. Although it has not been constant in all the projects, the first time I used it was in 2016.

I have never had any issues with the stability of Amazon Redshift. It has been very, very stable.

You can configure and scale it up when necessary. However, when I had to do it, it took a bit longer than expected. Overall, I would rate the scalability of Amazon Redshift a nine out of ten.

You can have the wizard, and you can start creating the cluster. You will have it running in minutes. From that point, you can start plugging into it and serving it as a source for the BI tool.

You can start small with a basic cluster to learn and practice with it. Selecting the most basic and economical cluster type can save you enough money to move forward with the solution or go with a solution in distribution for deployment.

I'd rate the solution ten out of ten.

The solution is used to handle unstructured data.

We have been using the product for some time. We are exploring and learning from the new offering of the product.

AWS provides an ecosystem of different offerings. The product offers good support for the data lake. It also provides a lambda function for automating flows.

The initial deployment was complex.

I have been using the solution for a year and a half.

We have an SLA of 99.99%. The product is available most of the time. The vendor maintains the SLA well. They also have a scheduled maintenance window.

The tool’s scalability is pretty good. I rate the scalability an eight out of ten.

The initial support for moving to a serverless database was very good. AWS provides good support. The technical support is not consistent, though.

We need a solution architect from AWS to help us with deployment.

Initially, we saw a return on investment. Now, the cost is going up according to our use cases. We need to optimize the cost.

The cost must be improved. We’re concerned about the cost. It’s driving a lot of TCO for us. We are looking for alternatives to optimize the cost.

Nutanix also provides similar products. It also offers different options for cloud providers.

It’s a pretty good solution. We plan small and grow big over time. Overall, I rate the product an eight out of ten.

I used it as part of the Amazon Connect integration; I had to implement Redshift for a couple of customers. It's used for various use cases involving reporting and exporting data to external sources. I have also used it for some analytics integrations.

The use cases I have typically worked on involve transferring Amazon Connect data to different systems for analytics. The two or three deployments I have done with Redshift are more or less similar because it acts as a kind of data middleware. 

Redshift effectively gathers data from various sources and facilitates the integration of that data into different destinations. This is typically used for insights collection, data showcasing, and integration into a standard ETL process.

So, the overall performance and speed of Redshift have affected the query times.

For the use cases I worked on, particularly on the Connect side, the query times with Redshift are pretty straightforward. We started using Redshift for these cases, and it significantly helped. To achieve faster results from Redshift, we first need to optimize the queries. It does reduce a lot of time in how data is gathered and then presented from the queries.

For me, the most valuable feature of Redshift is the way it operates as a reliable Amazon service. It has the capability to gather data from various Amazon sources and can be easily integrated with some maintenance configuration and code; Lambda functions are required for this. It can be used in multiple places. 

It all depends on the use cases, how we can actually ship the data, and how we can use the data from multiple sources. It is a typical reliable software and works very efficiently with Amazon.

For Amazon Connect combined with Redshift, the integration is mostly straightforward. Using Redshift always depends on the use cases, as there are other methods Amazon Connect can use to achieve its goals. As for Redshift itself, it can be used to build pipelines.

When working with third-party services requires additional integrations and configurations, which can sometimes add more cost.

From the Amazon Connect side of things, we have integrated Redshift. However, as an overall product, I have limited experience. 

But from what I have experienced, whenever we do a Redshift integration, it needs to be planned carefully because although Amazon supports multiple data sources and different data consumption, Redshift needs to be configured very effectively and requires dedicated shared knowledge for successful deployments. 

Redshift is a major service of Amazon and is very scalable. It enables faster recalculations and data management, helping to retrieve data quickly. It’s a relatively old service within Amazon's offerings, with at least 10,000 customers. I've seen cases in different organizations where users experienced up to 35X times increase in throughput while using Amazon Redshift.

It's pretty much straightforward. I just need some sort of configuration and a bit of integration, and then that's it. We should be able to get that done.

For first-time usage of Redshift, the process is pretty straightforward, thanks to the documentation provided by AWS and the straightforward integration with Amazon Connect. 

It didn't take me much time to create, deploy, and configure. It’s very straightforward. However, having some prior knowledge about Redshift can speed up the process significantly. 

For me, coming from a different background and learning about Redshift for the first time, I ended up reading some database documentation and doing some trials and testing before committing the production data.

For someone who knows a bit about how databases and data warehousing work, it's quite straightforward to learn Redshift. It's easier for those involved in analysis, reporting, and ETL data warehousing, specifically database developers or data warehousing developers; they can learn it faster. 

However, for someone without this background, it might take a bit more time to understand the concepts and how they integrate in different ways.

Overall, I would rate it an eight out of ten because it has been straightforward for my use cases. It's easy to integrate for those use cases.

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.

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 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.