Amazon Redshift Reviews

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.

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 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 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.

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.

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.