Amazon Redshift Reviews

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.

I use the solution in our company's project. Currently, our company uses the tool to connect two parts. Firstly, my company uses Amazon Redshift by connecting it with AWS Glue to create S3 files. My company is involved in the creation of external tables as we are shifting the data into Amazon Redshift. Secondly, my company uses the database, and we have written the code by creating the external tables while storing the data on Amazon S3.

Amazon Redshift's main advantage is that in our company, we can easily document it whenever we put in some data, meaning we need to take care of the documentation. The tool can also be used for data errors, so we can see the query number of records, and it is also very easy to use for queries.

It is not easy to deal with queries in Amazon Redshift and see the same data. In our company, we can easily create a query, but the problem is that when we are doing the extractions, we need to do them for analytics purposes, and that is when Amazon Redshift gets a little bit slow.

If any tools are available in the product for analytics purposes, it would be good. If we are able to integrate the tool into Excel or something, it will be good. MySQL and some other databases can be directly integrated into Excel for analytics purposes, and if Amazon Redshift could have such functionalities, it would be very easy for analytics.

I have been using Amazon Redshift for two years. My company just uses the tool, and we are not Amazon partners.

It is a stable solution, but it will be a bit tough when we do the extraction.

Amazon Redshift comes in two parts. One is the external tables, and the other one is the internal tables. Mostly internal tables, we need to buy storage from our Amazon Redshift. If space is needed for the external tables, then we can use AWS S3. If you have the necessary budget, you can store a number of files, and they will be easily stored on Amazon Redshift.

I rate the technical support an eight out of ten.

Positive

My company mostly works with MySQL databases as it is a worldwide classic tool.

MySQL is a classic tool with a lot of functions, and it can integrate into separate tools, like Excel or something, but such functionalities are not available in Redshift.

I only used the product to create and store data, but I never got involved with the installation part.

Our company uses Amazon Redshift since there are many data engineers. The data engineers deal with shifting data into Amazon Redshift. My company also uses the data visualization tools connected to Amazon Redshift and the product for dashboard-related purposes. Amazon Redshift mainly supports external tables in our company. Our internal projects use only the external tables created with Amazon Redshift so that the data can be stored anywhere. For querying, my company uses Alation.

With the APIs that are available, it can be easily integrated with other tools.

It is a good tool, and if there is more data and users, it will be very easy for analytics, particularly when extending the data analysis and seeing that in Excel. If you have all the functions related to the tool, it will be good.

One needs to fully explore the products available in the market. Amazon Redshift is the same as MySQL with all the functions. MySQL allows users to manage a huge amount of data, but in Amazon Redshift, when it comes to analytics, the tool has certain shortcomings where improvements are required.

I rate the tool a six 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.

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.

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. 

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.

I have used it for our reporting solution requirement. We gathered data from different processes and applications, like the high system process. Clients can review the data; we use it for connections and reports. Additionally, Redshift generates some configuration files without using an application.

For reporting purposes, Redshift is a great tool to use. Redshift has an advantage when it comes to administration, making it easier to manage and collaborate. Additionally, its server architecture allows for faster processing. Redshift also supports prepaid costs, which is another great feature. However, similar features are also available in Azure.

Redshift has some advantages in terms of administration and performance.

When compared to Snowflake, Amazon Redshift does not have the capability to dynamically increase the VM file. However, Amazon Redshift provides a virtual database called 'VW' that allows you to increase the size of the warehouse to run faster on a monthly basis without changing anything. This feature is not available in Redshift. So it's a limitation of Redshift.

It's not possible to immediately increase the virtual warehouse size in Amazon Redshift. When compared to Snowflake, we cannot increase the virtual warehouse size in Redshift. 

I have been using this solution since 2015. 

I don't see any issues with data loss or any other problems. Although there might be some loss in the data center, we monitor it, and everything is enabled. In such scenarios, the turn-up time is much faster. We've been using it since 2018, and I've got the same product for another customer using a limited rate. So, I don't see any significant impact, and it's a very stable product.

We have to consider the scalability of this solution carefully. In production, we have a proper size. We allocate 40% for data storage and 60% for temporary segments. We cannot increase data storage usage beyond 50%. It cannot exceed 80% of the total utilization, including network speed and query performance. We monitor all of these carefully.

So if the CPU utilization goes beyond 80%, I recommend upgrading to multiple nodes. It ensures that there won't be any issues. Around 30 people are using AWS and Azure modules along with me.

We have contacted the Redshift team for support related to other installations, such as WDL configuration for project implementation for a web application. When it was not working as expected, we had to provide authentication for the web chat. So, we used to contact them for that kind of knowledge.

The initial setup depends on who is doing it. In my opinion, it doesn't require much knowledge. Since we've been using it for a long time, it's much faster for me, but it might not be the same for others.

It's on the expensive side.

One reason we chose this solution is that we are in the process of moving everything to the cloud. But that's not the only reason. My company wanted to consolidate everything into one system, and AWS provided all the necessary information in one place. For example, Lambda is for specific storage and limited membership; all this information is available in one cloud network. This way, data segregation is much faster and easier to use. It's just everything in one cloud network, so we decided to use it.

I would recommend it. However, I think we need to consider other configuration levels. You need to decide, and I would not go with the first option.

To evaluate the data you are planning to migrate, we need to assess the environment. What is the value of your data, and what type of data is it? The density of the data is also important. Before implementing Redshift, we need to ensure that the AWS configuration is activated. After that, you need to set up enrollment and increase your storage. I don't recommend making a purchase on the same day, but it is a critical moment at a high level.

I suggest purchasing a renewal that meets the deposit requirements so that you can have a good experience and optimal performance. You can increase the budget for the building process. If you have the right team, such as those with experience in AWS or those who are learning about Azure databases, they can start using Redshift without any issues.

Overall, I would rate the solution a nine out of ten.