Amazon Redshift Reviews

For the on-premises version of Amazon Redshift, we need to start from scratch. However, with the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features.

They have enriched and elaborate documentation that is helpful.

We are using third-party tools to integrate Amazon Redshift, they should create their own interface on their own for it to be easily connected on the AWS itself.

I have been using Amazon Redshift for one year.

Amazon Redshift is reliable and has high availability.

The scalability of Amazon Redshift is good.

We have approximately 20 people using this solution in my organization.

Before we were using Amazon Redshift, we were working with Postgres, Greenplum, and Oracle SQL. These were on-premises databases, and we migrated to the cloud.

The price of Amazon Redshift is reasonable because it depends on the usage that you use and for DWH for the long term.

I would advise others that if they have a large set of data where you have a less number of updates, then choose Amazon Redshift. If there is more update and fewer inserts, then do not use Amazon Redshift.

I rate Amazon Redshift an eight out of ten.

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

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

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

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

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

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

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

It's very scalable.

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

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

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

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

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

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

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

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

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

We mainly use the solution for marketing analytics.

The solution works fast. I use Redshift to clear a lot of web page data. I use it mainly as an extraction tool to obtain the information I need for a project and store it in parquet files on a disk. Later, I work on the data using Python. I write back all my final results to Redshift and store the temporary files on a local machine.

They should provide a structured way to work with interim data than to store it in parquet files locally. Also, Redshift is unwieldy. There should be better integration between Python and Redshift. It could be more accessible without using so many sequels.

They should make writing and reading the data frames into and from Redshift easier. The performance could be better. I have used Redshift for extensive queries. For the large tables, it's easier to unload to Redshift, but subquery tables that run complex grids are slower for configuration. I have to use the unloaded command to unload the whole table. Further, I have to read the table into a server with extensive memory in Python and process the data ahead. It's not optimal. 

We have been using the solution for two years.

I rate the solution’s stability as a nine.

The solution is scalable. It handles different loads very well. We have 80-100 users using the solution in our company.

The setup was quite complicated. For instance, if you use AWS Glue to automate loads into Redshift, setting up the security for the requirements between the two is complex. I've struggled a lot to set up the cluster on VPC and to get all the endpoints set up correctly with the right access and services. Especially from Glue's endpoints, I had to repeat the same process every time. It consumes a lot of time. In comparison, the CloudOps executives do the setup very quickly.

I have heard complaints about the solution’s pricing, and thus I rate it as a five.

I rate the solution as an eight out of ten.

We were using the solution for our data backup, but we wanted to optimize it, so we turned to AWS Glue. Amazon Redshift wasn't really great for us and wasn't working out.

Amazon Redshift was used for data storage while moving back from S3 to Amazon. However, it lagged, taking its own sweet time for data backups which also depended on the server location. Because of the aforementioned reasons, we started losing a lot of data that wasn't even real-time data. Ultimately, this affected our analytics at the end of the day. Also, we have been trying to do some work on our AI models, which emit out recommendations based on the live dataset. There were a lot of lagging issues. So, for example, sending out somewhere around 0.1 million or 100,000 emails used to take almost 12-14 working days, and this also includes the process of pulling all the data and sending them to CronJobs. Since we wanted all this work to happen in real-time, we had to get rid of the tool.

I would like Amazon Redshift to improve its performance, analytics, scalability, and stability. Other than these points, I am not aware of any other areas to address since Amazon provides a variety of independent services for their customers to choose from, and if one were to express dissatisfaction with Amazon Redshift, Amazon would likely suggest AWS Glue as an alternative. Similarly, if another issue arose, Amazon might recommend Amazon RDS. There are a lot of things they try to upsell to you, each with its own pros and cons and in different packages offering different perks. So, it all depends on your business needs and what you choose for your business. I wouldn't criticize Amazon for this because they have created packages tailored to their customer's needs, which helps to prevent customers from looking elsewhere.

I have been working with Amazon Redshift as an implementer for three years.

Stability-wise, it has a lot of issues with threats, and that is why we went for a threat shift optimization. In short, I mean to say that it is not stable at all.

If you require a highly scalable solution, I would not recommend Amazon Redshift. We currently have 12 clients using Amazon Redshift, and the scalability of the solution is terrible. In terms of scalability, I would rate this solution a three or four out of ten.

Amazon Redshift has a lot of documentation, but the setup is fine. Three years ago, the solution's deployment process took over a month or a month and a half.

Every solution has a cost and comes in different packages. Considering these factors, AWS Glue is on top. Though Amazon Redshift is good, it depends on what kind of business you're trying to do, what type of analytics you need, and how much data you have.

For Amazon Redshift, we pay around INR 60,000 annually. The cost factor also depends on the number of existing customers. In addition to the standard licensing fee paid for AWS, we incur a cloud storage cost of around a quarter million for the amount of data. We also have to bear additional costs for data security and cybersecurity, which are well taken care of by Amazon, hence the premium pricing. There are several other features and services provided by Amazon that justify the premium pricing.

Amazon Redshift is a horrible solution. I recommend my customer to use AWS Glue since while dealing globally with real-time data, which you need to make decisions, factors like how much cost and data is needed to make a decision should be considered. Apart from this, if customers are paying a huge price for the solution, then probably Amazon shouldn't mind spending on the tool. However, it may not be necessary for small businesses with only a few thousand data points. Although Azure is a better option, some clients prefer AWS, and we had to develop a solution using AWS for our client. Overall, I rate this solution a three or four out of ten.

It is scalable, easy to use, and has very good query performance. It is serverless as well.

They introduced machine learning directly in to Redshift, and you can now query using machine-learning functions.

It's very easy to migrate from other databases to Redshift. There are migration tools dedicated for this purpose, enabling migration from other databases like MS SQL directly to Redshift. The majority of the scripts will be automatically transposed.

AWS Snowflake has a very good feature for cloning databases. It makes it easy to clone a data warehouse, which is useful. I would like to see this feature in Redshift. 

We have been using Amazon Redshift from 2013.

For stability, I would give a rating of ten out of ten.

We have 16 Redshift users in our company, and I'd rate the scalability at ten out of ten. 

Redshift's technical support has been great.

The initial setup is simple.

In comparison to the price of similar solutions, Redshift's cost is the lowest.

I would recommend Amazon Redshift and would rate it at nine on a scale from one to ten, where one is the worst and ten is the best.

We use Microsoft Azure, but one of our clients is on AWS, so we did a POC for Amazon Redshift. Now, it's up to them to make the call on whether to continue with the solution based on pricing and their needs.

Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there. 

Redshift's GUI could be more user-friendly. It's easier to perform queries and all that stuff in Azure Synapse Analytics.

I have been working on Amazon Redshift for six months, but I haven't used the solution extensively because we only did a POC for one client.

I think Redshift is stable. There shouldn't be any issues. 

Redshift is serverless, so you can scale it as needed. If the amount of data increases, the nodes will automatically be connected.

We haven't contacted Amazon support yet because we didn't encounter any issues that required support from Amazon, but I imagine it's comparable to the service we get from Microsoft. 

I've worked with a couple of cloud-based database solutions like Amazon Redshift and Azure Synapses,

Setting up Redshift is a little complex and requires some prior knowledge, whereas Azure's GUI is more intuitive and it's easier to navigate the steps.

Redshift is a managed service for data warehouses.

I find the most valuable features to be the MPP style of processing, which mostly all of the data warehouses provide. The ability to integrate all other AWS services, such as NSS and S3, with little effort is very helpful. The service is well maintained, there are update patches frequently.

We recently moved from the DC2 cluster to the RA3 cluster, which is a different node type and we are finding some issues with the RA3 cluster regarding connection and processing. There is room for improvement in this area. We are in talks with AWS regarding the connection issues.

In an upcoming release, I would like to have a Snowflake-like feature where we can create another cluster in the same data warehouse, with the same data. You can create a different cluster and compute nodes for each of your use cases, for retail, and for your data analyst all while keeping your underlying data safe.

Additionally, the cluster resize process takes down the cluster for too long, approximately 15 minutes. There are limitations to the size, you can resize only by a multiplier of two, for example, if you have four nodes then you can either go to eight nodes or you can come down to two nodes. There should be fewer limitations.

I have been using Amazon Redshift for approximately one year.

There are three people in my organization using this soltuon.

Their technical support is good. They provide assistance when we need it but since we were experiencing a connection issue it was taking longer to get a resolution. We had to involve a vendor to get it resolved for us.

I have previously used ClickHouse.

The installation was easy.

The price of the solution is reasonable. According to the RA3 cluster particularly, it provides 128 GB of storage with only four nodes. If you can manage your computations processes with the help of materialized views and proper queries. I think the IP clusters are very useful and overall fair for the price.

We have been evaluating Snowflake and are in the POC phase. If it passes our quality tests then we will be moving to it soon.

I rate Amazon Redshift a seven out of ten.

We use Amazon Redshift as our data warehouse.

I found the Amazon Redshift computing services easy. I found the computing instances the most incredible in the solution.

What would make Amazon Redshift better is improvising on the pricing structure. For example, Acronis provides backups in cybersecurity, yet the pricing is a bit lesser than Amazon Redshift. I was comparing AWS, Amazon Redshift, Azure, GCP, and Acronis, and I found that Acronis has a lesser price than the other solutions in the archiving space.

I have at least two months of familiarity with Amazon Redshift.

Amazon Redshift is a stable solution.

Amazon Redshift is a scalable solution that also suits enterprise companies.

I haven't contacted Amazon Redshift support because I'm not a client, but according to my subordinates, the support is excellent.

Amazon Redshift is very easy to deploy, and deployment-wise, it's impressive. It only takes two to three minutes max to deploy.

Pricing for Amazon Redshift is reasonable, though it could be somewhat higher than other solutions, such as Azure. Still, when you base your comparison on the services offered and the pricing, it's the most reasonable versus its competitors, such as RDS.

My company provides backup solutions.

I'm a business analyst with little expertise in a specific service or solution, but I've gone through all the solutions.

I have experience with Amazon Redshift.

What I'd tell others looking into implementing Amazon Redshift is that it's one of the market's best databases or data warehouses right now.

My rating for Amazon Redshift is nine out of ten.

I'm an Amazon partner.