Amazon Redshift Reviews

Amazon Redshift serves as our data warehouse system. We collect data from various sources, including 153 streams. We gather companies' data for rating, deployment, and stock market analysis. We then push this data onto Amazon Redshift, which Power BI, Tableau, and even Google Looker use for reporting and analysis.

The tool's most valuable feature is its parallel processing capability. It can handle massive amounts of data, even when pushing hundreds of terabytes, and its scaling capabilities are good.

The solution's performance and speed depend on the data structure and model, but Amazon Redshift's massive parallel processing is typically good. 

In terms of improvement, I believe Amazon Redshift could work on reducing its costs, as they tend to increase significantly. Additionally, there are occasional issues with nodes going down, which can be problematic.

We often encounter issues like someone dropping a column or changing the order of columns, which can cause synchronization problems when pushing data through our pipeline. It's a minor issue, but it can be annoying.

I have been using the product since 2019. 

The product is stable. 

My company has more than 100 users. 

I've reached out to the support team for help. We used to connect with them almost every week. They're very quick to respond and provide assistance.

The tool's deployment is straightforward. You need to know what kind of clusters and nodes you want, how big they should be, and how much money you're willing to spend.

The tool's ROI depends on the data. If you have too much data, it's worth it. If you have some data and don't need it, you're wasting money.

I rate the overall solution an eight out of ten. I'd recommend Amazon Redshift. It's stable and scalable, needing little maintenance. It handles data well and provides fast reporting results.

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.

We use Amazon Redshift in our business intelligence ecosystem. It's simple to configure, cost-effective, and close to our data sources.

The managed service is scalable and reliable. AWS takes away scalability and reliability components, making it relatively easier for us.

Its simplicity in configuration, cost-effectiveness due to being in the cloud and close to our data sources, and the fact that it's a managed service that is scalable and reliable are highly valuable.

There are no significant issues preventing us from doing our tasks. However, there might be some limitations from a business intelligence perspective, but nothing we can't find a workaround for.

We have been using it for five years or more.

We are happy with it, so there are no major stability issues that stand out.

AWS handles scalability and reliability, making it easier for us to manage.

We have two people to continue with support.

Positive

Setting it up was straightforward due to its simplicity and being a managed service.

AWS handles the scalability and reliability components, making it easier to implement.

Ensure that information about specific configurations and internal uses remains anonymous.

I'd rate the solution 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.

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. 

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.

The solution can be used as a warehouse. We dump any data that exists in our company into it. We spring up different databases based on the requirements.

The tool is the one place where the company’s data is stored. We use it extensively. Previously, we had data in multiple databases and structures. Since we started using Redshift, everything is in one place. We have multiple supporting layers and dashboards connected directly to Redshift. It helps and supports all the different businesses.

It is very easy to dump data into the tool. We do not worry about partitions. It has auto-vacuum features. We need not worry about I/O speed. It is easy to integrate the solution with other AWS services like Blu and Pacemaker.

Sometimes, it's difficult to get the metadata from Redshift. The product has too many layers to get simple information. Redshift does not have primary-key tools. The vendor must consider adding them.

I have been using the solution for two years.

The stability depends on the number of clusters and the query performance. If the cluster is scaled enough, Redshift queries are pretty quick. I rate the tool’s stability an eight out of ten.

We have multiple issues with the product’s scalability. The AWS team has helped us scale to the correct amount. We do not use Auto Scaling. We use AWS experts to help us scale based on our needs.

The setup is pretty easy. I rate the ease of setup a ten out of ten.

I will recommend the solution to others. It is pretty easy to use. If we're using AWS, it is easy to use other AWS features. 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.