Amazon Redshift Reviews

Amazon Redshift serves as our primary analytics solution, as we receive a large amount of chunk data daily from different restaurant sites. We also use Amazon Redshift as a cloud data warehouse for storing and analyzing a large set of data, including loading data from S3 and other sources into Redshift, and then using SQL querying and other reporting tools to provide insights to business users.

Amazon Redshift fits into our analytics and reporting process as S3 is the source table for us. We load the data into Redshift and SQL Server, which are our primary target tables, and we are using Informatica PowerCenter and SnapLogic tools to perform transformations while simultaneously transitioning from on-premises to cloud platforms.

Amazon Redshift offers massive parallel processing capability, allowing it to handle millions of records, along with integration with other AWS features such as S3 and QuickSight. The column-level storage supports high performance and easy data retrieval compared to other tools.

The integration with S3 has helped my team immensely since S3 is our starting source, and the direct integration with Amazon Redshift simplifies the transformation and loading process. Columnar storage has benefited our performance significantly, allowing us to extract specific rows from tables with years of data much faster than other tools with row-level storage.

Amazon Redshift has positively impacted our organization by significantly improving query performance and speed. Features such as easy integration with S3 allow us to process millions of records efficiently, ultimately saving us nearly one hour per day for our project.

Amazon Redshift could improve by enhancing its UI to be more user-friendly for non-technical users and by offering better cost management, as similar tools tend to be less expensive. Additionally, integration with AI features could elevate its capabilities and popularity.

I have been using Amazon Redshift for the past eight months.

Amazon Redshift has been stable with no crashes or reliability issues since I joined this organization.

Amazon Redshift's scalability has met my expectations, especially with the recent added data volume from new restaurant sites. We have successfully increased our storage space, which was a smooth process without server crashes before or after scaling.

We recently scaled Amazon Redshift storage by 500 GB to accommodate the additional data from approximately 20 to 25 new restaurants. This demonstrated that the backup feature and scalability in Amazon Redshift were crucial during this process.

My experience with Amazon Redshift's customer support has been positive. When I needed setup assistance and credentials, they quickly helped me and provided documentation that allows anyone with prior knowledge of Redshift or SQL to resolve technical issues.

I have not used previous solutions in this organization, but I also utilize SQL Server, Oracle SQL Developer, and GCP BigQuery. In comparison, Amazon Redshift excels in query performance, scalability, and security, ranking it second only to GCP BigQuery.

When I first joined my team a year ago, they had been using Amazon Redshift for the past five to six years. While I did not face any initial issues, I am uncertain if my colleagues experienced any difficulties during the initial setup.

We earned back our investment in Amazon Redshift within the first year. Initially, there were 25 employees for development, but now only four maintain Amazon Redshift daily, saving significant resources and allowing us to use the saved time for other tasks.

My experience with pricing and setup cost is satisfactory, as we have been loyal clients of Amazon since the beginning of our organization. However, I am not aware of the exact costs associated with our project since I am not part of the management team.

My team did not evaluate other options before choosing Amazon Redshift, as it integrates seamlessly with our source, Amazon S3, making it a straightforward choice compared to other cloud tools.

One unique aspect of our workflow with Amazon Redshift involving our main use case is that I resolved an issue related to bundle data in our restaurant's profit sheet. I queried a few tables in Redshift and discovered that while coupons were applying, they were not yet added to our main base tables, which we later corrected.

The performance of Amazon Redshift when handling large data sets or complex queries is impressive. Due to its column-level storage and parallel processing, we can quickly extract data from databases with millions of rows.

Amazon Redshift integrates well with ETL tools such as Informatica PowerCenter and SnapLogic, transforming data before loading it into target tables. From those tables, we use Looker dashboards or FreeForm and Essbase to report insights to business users.

My advice for others considering Amazon Redshift is to choose appropriate distribution and sort keys as crucial steps in its integration, utilize the copy command for bulk loading from S3, review the documentation for a better understanding of features, and consider Redshift Spectrum to reduce storage costs while reallocating resources for other tasks.

I have covered all the important aspects, including features, disadvantages, and what I appreciate about Amazon Redshift. I would rate this product an 8 out of 10 based on my overall experience.

We handle large data for insurance companies, and Amazon Redshift serves as a data warehouse where we store all the structured data. We also use it for checking analysis and getting insights from the data. We pull the data from Amazon Redshift using Athena or different tools within Amazon.

It functions as a place where we store a large amount of data in the form of tables for simple understanding.

The most valuable features of Amazon Redshift include everything being integrated within Amazon, making it really easy to run queries faster, allowing us to retrieve data as soon as possible compared to other tools. Its friendly functionality connects with all tools linked to Amazon, and the data retrieval is very fast, making it a good warehousing tool.

The specific features of Amazon Redshift that are beneficial for handling large data sets include fast retrieval due to cloud services and scalability, which allows us to retrieve data quickly.

Scalability is the best feature of Amazon Redshift. Amazon Redshift handles scalability automatically, so we do not need to scale up or down; it is all managed by Redshift.

Everything we have is really great, and I am not finding any points for improvement as everything is good for our use case.

For minor enhancements, regarding system properties, it is all managed by Amazon, but the user interface could see slight changes to be more user-friendly.

Integration with AI could be a good improvement. Amazon Redshift might support our AI-driven projects or big data workflows, but we are not using it currently.

I have been using Amazon Redshift for 1 year.

I find Amazon Redshift to be very stable. I would give it a rating of nine.

I rate the scalability of Amazon Redshift a perfect 10.

I would rate the technical support for Amazon Redshift a nine. Whenever we need support, if there is an issue accessing stored data due to regional data center problems, the Amazon team is very helpful and provides optimal solutions quickly.

Positive

Before choosing Amazon Redshift, I have used a couple of data warehouses, one of them being Snowflake.

I would rate the initial setup of Amazon Redshift a six. During the setup, whenever we try to give any permissions, if I have a data warehouse in Amazon Redshift and want to provide access to a user to just check the data without making any changes, it is a long process to create a policy in Amazon and share it with that policy. Creating various permissions can lead to confusion and requires a lot of manual work from our end.

To deploy data into Amazon Redshift, creating a Redshift account and the place to store data can be done by one person.

If you want to push raw data and store it in Amazon Redshift, then a large team of data engineers is needed to check processes and pipelines, but creating an account can be done by one person.

I use Amazon Redshift as a Pro enterprise version internally in my company. I use other Amazon tools but cannot review them now since it is just the beginning and I need more experience before giving feedback. My overall rating for Amazon Redshift is 8 out of 10.

I have dealt with Amazon Redshift for several years, and I can describe in general what the main use case for this product is.

I work in the real estate industry, and the best features I appreciate about Amazon Redshift include its efficiency in managing large datasets.

Amazon Redshift's security features help meet our compliance needs, and I have experience with its pricing and licensing which I find reasonable.

I utilize Redshift's workload management.

There are areas of Amazon Redshift that could be improved, but I appreciate the current features.

The length of time I have used the solution was mentioned in the text.

I used another solution before Amazon Redshift and switched because it did not meet my needs. We compared other vendors before choosing Amazon Redshift.

The initial setup for Amazon Redshift is often complex. Sometimes they have difficulty understanding what issues we are experiencing.

The solution requires just a few people for maintenance. The job roles that maintain this include data engineers and database administrators.

I use Redshift Spectrum, and it has significantly helped in managing my analysis queries on S3 data. On a scale of 1-10, I rate Amazon Redshift a 6.

We use Amazon Redshift for our data lake. It serves as our organization's data mart.

Amazon Redshift is a combination of features. It is basically a cluster that works better with Glue when it comes to incremental loads. It can handle a serverless architecture, and at the end of the day, it is a database. Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.

Amazon Redshift does not work alone and requires integration with other AWS tools like S3 and Glue for a complete ecosystem. They should bring the entire ETL data management process into Amazon Redshift. Either they should move Amazon Redshift under Glue, or Glue should be brought under Amazon Redshift. The initial setup could also be improved, particularly in terms of sizing, as it requires trial and error to get right.

I have been working with Amazon Redshift for three and a half to four years plus.

Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.

Amazon Redshift scales well. However, the scalability part needs improvement as the sizing requires trial and error. After assigning resources to Amazon Redshift, it relies on experience and cores. Clusters and cores are factors, whether using a higher core with a lower cluster or two clusters with a lower core.

Amazon tech support is a paid service. Its primary functionality is basic, and if I pay, they provide support.

Neutral

I used Anaplan previously. I stopped working with it because I shifted to data analytics.

The initial setup is not complex, but sizing is a bit difficult. Choosing the right server size requires trial and error. Once resources are allocated, experience and cores determine the appropriate configuration.

I would rate the price of Amazon Redshift seven out of ten. It's a pretty good price and reasonable for the product quality.

Currently, Microsoft Azure is more preferable due to its single ecosystem.

Amazon Redshift users typically consume data from it rather than being directly involved with it. It is more suitable for organization-wide use, but the number of users actively engaged with it is usually five or six. I would rate Amazon Redshift eight out of ten overall, and I can recommend it to other users. Overall, I would rate the product eight out of ten.

I am providing resources to our developers and application team. We use Amazon Redshift from a machine learning perspective. We are currently evaluating some tools for machine learning.

I am using Amazon Web Services from a machine learning perspective. They provide some tools, which we are currently evaluating for machine learning. With the tools we have, Amazon Redshift has the features to query efficiently. Security configurations are implemented across all processes, such as AWS Config and GuardDuty.

As of now, I cannot comment on that because we are still evaluating whether to adopt it or not.

I have been working with Amazon Redshift for more than five years.

Scalability is not significantly relevant to us as of now.

I rate their support seven out of ten. It's costly when you enable support. The price is high for technical support.

Neutral

I did not work with anything similar to Amazon Redshift before.

Currently, we are waiting for the right data. We are procuring the data from a government perspective. Once we have our data, we can evaluate it from this perspective.

Price is not a bottleneck for us. The cost of technical support is high.

I recommend Amazon Redshift depending on the organization's requirements. I rate Amazon Redshift six out of ten. We are waiting to procure raw data to evaluate our workflows and achieve dashboards with visualization tools.

We primarily use Amazon Redshift for visualization or connecting to our BI tools, such as Tableau and Power BI. Nothing beyond that.

As a developer and for our users, we have had a good experience connecting Tableau or Power BI to AWS Redshift and building visualizations and BI analytics reports. Migrating from on-premises to cloud, we have found good performance on Redshift as well.

The seamless connection and good scalability on the cloud are valuable features of Amazon Redshift. Additionally, the ability to create a lot of views or materialized views is beneficial.

Improvement in the immediate response and the process of getting into a call could be helpful. We have had to wait for at least twenty-four hours to get a call and then wait for a couple more hours for a solution. Improved connectivity to different BI tools and already published connectors for major tools in AWS could enhance the service.

We have been using it for approximately one to two years.

We have not experienced any major challenges with its stability.

The scalability on the cloud is good unless costs become too high. Serverless Redshift and scaling are beneficial.

When we raise a query, we get a response, and sometimes we request a call which helps us solve the issue. However, there is always scope for improvement, particularly in reducing the wait time.

Positive

I have been using Tableau for a long time. I have also used Business Objects three years before. Now, I only use Tableau.

There is no substantial challenge in installation as it is more or less a cloud service, which can be done easily.

I do not have an idea around ROI.

I have no idea about pricing.

We did not evaluate other solutions for a comparison with Redshift.

We have not used any of Redshift's AI features. Generally, on a scale of one to ten, I would rate Redshift as satisfactory.

I'd rate the solution nine out of ten.

There are many use cases, as I've worked with Amazon Redshift at different companies. Initially, we used it as part of the AWS suite, which made it easy to get started. We were able to move data from MySQL and PostgreSQL into Amazon Redshift, and we even used it in a production environment. However, the scalability of Amazon Redshift was not enough for our needs, so we switched to Snowflake.

The most valuable feature is its seamless integration within the AWS ecosystem. If your data is already in S3, it becomes easier to port it into Redshift and work with it. 

Redshift allows you to transform different data formats and consolidate them into one Redshift cluster. This means you can transform various siloed data sources like Excel files and CSV files into Redshift. 

With Redshift, you get a suite of applications that enable you to drop a file and consume it into the data warehouse, facilitating the use of Power BI dashboards, Tableau dashboards, or custom dashboards on top of Redshift data. This setup process is much smoother compared to other tools that may require going through legal MSA (Master Services Agreement) and other complex procedures.

As our scalability requirements and data growth exceeded expectations, Redshift didn't scale up to meet our business needs. So, at that point, we made a switch to Snowflake, which provided the scalability we needed. So, scalability is one area of improvement.

One area where Amazon Redshift could improve is in adopting the compute-separate, data-separate architecture, which Delta, Snowflake are adopting, and a few others in the cloud data warehouse spectrum. Although Redshift introduces Aqua to achieve some level of scalability, I still feel that when it comes to scaling up, whether it's vertical scaling or horizontal scaling, there is a noticeable amount of downtime for end consumers. So, if I need to switch from DC1 to DC2, or from one compute/storage optimization to another, I have to bring down the entire cluster and then bring it back up. That's a pain point.

I have been using Amazon Redshift since 2012.

In terms of stability, I would say Redshift's workload management is generally stable. However, the way Redshift distributes the workload is not as scalable. I often don't have clear visibility into what queries are on hold, their priorities, or other relevant information. 

On the other hand, Snowflake provides more transparent insights into the queue and running queries, and it also informs me when the queue will be drained. Snowflake's multi-cluster approach to handling workloads surpasses Redshift in these aspects.

I would rate scalability a seven out of ten. There is a need for improvement in terms of scalability. 

I did contact customer support regarding a data breach. In terms of handling such incidents, I found their customer support to be excellent. They were proactive in ensuring that our account and data were not compromised. They guided us through the necessary steps to recover and resume operations. 

Positive

The initial setup is seamless. You just need an AWS account for the setup. I would rate my experience with the initial setup a nine out of ten, where one being difficult and ten being difficult to setup. 

If I have to scale maintenance, maintenance is much easier in Amazon Redshift, provided you have data governance in place and constantly monitor the usage and things of that nature. The roles and the RBAC are not so convenient because everything goes to IAM. Whereas Snowflake has flexibility in setting up RBAC, which is entirely different than what Amazon Redshift has to offer.

If it fits the bill and the business purpose, I would recommend using the solution. But when it comes to business needs, that's when all these things matter. Whether one tool versus the other supports all the business needs and meets the SLA and RT, or whatnot, then we are talking about different products. So it's based on business needs. 

Overall, I would rate Amazon Redshift a six out of ten. They still need to adapt the maturity and be on par with the open source community and ecosystem. Recent technology adoption is towards Delta. Microsoft is moving towards Delta. A lot of ecosystems are moving towards Delta, but Amazon Redshift is not making its move towards this technology, which is serving the engine. So that aspect has to grow.  So, there are a lot of aspects where they can improve.

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.

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.