Amazon Redshift Reviews

I'm the head of Data Warehouse and Business Intelligence, and our company is an Amazon customer. Most of the company's data sources were on Amazon at the time the product was deployed so it was logical to use this database. The data warehouse is quite small, compressed it's maybe 160 GB. It's not like an autonomous data warehouse or Exadata, which was almost 40 terabytes. It's a simple method of achieving extraction and loading. There is no real incremental load on this. Of course in the future, with the company growing, this should be changed and we'll probably need some kind of incremental system instead of this approach.

The product is relatively easy to use because there is no indexing and no partitions. There is no referential integrity only declarative, which is okay.

From my perspective, the product could be improved by making it more flexible. There are now more flexible products on the market that allow for expandability and dynamic expansion as the market changes with regard to data warehouses. Although the product is simple to use there can be problems. If you declare some unique key in a column and then store it, the database is going to believe this is what you have and results will be distorted. It's fine if the query is simple but if it's complex or you have too many queries per hour, it can create a bottleneck for Redshift and then you can't return and recover. It requires some fine-tuning. 

For additional features, I would like to see support for partitions, it doesn't exist yet as a feature. It's quite an important issue when you're dealing with large databases. Also, I believe the product needs improvement in parallel threading to support more database users without jeopardizing performance.

I've been using Redshift for about a year and a half. 

We sometimes have issues with stability, especially over the weekends. It performs, of course, but sometimes there are problems. 

In terms of pricing, if you plan to use the product for a small company and you compare the cost to a product like Oracle Autonomous Data Warehouse, then you need to consider potential company growth and whether you may need to expand the system in the future, require partitions, etc. It could be that in the end, Autonomous Data Warehouse is cheaper than Amazon because if you use that product it can fit your processes and run dynamically. This is not possible on Redshift. If you have two different types of servers on Redshift then at some point you'll reach a limit and will need to move to another type of cluster which is not such an easy operation. On the other hand, with Autonomous Data Warehouse, you can add extra terabytes, but that's it. Nothing else. 

I would rate it a five 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.

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.

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 Redshift Spectrum for creating temp tables during the Ignition process.

I have primarily used the Redshift Spectrum feature and found it most valuable.

During our last office project, Redshift couldn't perform well even for a data size of 6 TB. Thus, compared to Teradata and Snowflake, the solution needs to work faster. They should extend the plan by including better optimization and readability as we get while using Teradata. Also, they should provide zero-copy coding and sharing facilities.

I have been using the solution for three months.

I rate the solution's stability as a seven.

There is no issue with the solution's scalability.

The setup was straightforward as I have a POC. It was a simple process.

The solution is available at a mid-range price as compared to other vendors.

While using Redshift, we need to combine it with Glue to complete the process. Whereas, Databrix offers the same procedure without combining two solutions. Redshift would work well with small businesses if they already use AWS services. They can use Redshift if the database is not that huge. I recommend Snowflake over Redshift. I rate the performance as well as the overall product as a five.

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 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.