Amazon Redshift Reviews

I use Amazon Redshift for the creation of SQL ETL procedures in a business system. Business people check this in a front-end application, and it helps them plan sales for the next year.

Redshift is being deployed on a Microsoft Azure server.

There are about six people working on this project and using the solution, but there are many similar projects running on Oracle and Redshift.

I like it because the usage is very similar to Microsoft SQL server. The structure of the query and the temporary tables are very similar. Until recently, I thought it was the superior database, but now I think that Redshift is better.

The explain panel in the Redshift database could be better.

I have used this solution for 10 months.

The solution is stable. I haven't had any problems or downfall with the database in the 10 months that I have used the solution.

I have also used Microsoft SQL server and Oracle.

Setup was difficult because I had to set up 25 connections with different users and passwords. The connections have been predefined, but there were still problems when trying to connect for the first time. I had some problems with some certifications that were malfunctioning. This might have had something to do with the functionality of my keyboard because if I pushed a random combination on the keyboard, it would delete the certificate from the folder and the connection wouldn't work. I think this is a problem with the remote desktop rather than with Redshift.

I would rate this solution as eight out of ten. I can't give it a higher score because there are some issues with variable character columns in the table. Otherwise, it's a great database.

Some of my former colleagues from a previous job have joined my organization, and they have had some issues with the SQRs because some things work differently in Redshift, like the partition bar. If someone has issues with Redshift, my advice is to check with support.

We use Redshift as a central data warehouse. This data can be consumed directly by AWS services or different applications where we can provision the data via Athena. If we want to create more business intelligence tools and analysis, we write some SageMaker notebooks for deploying machine learning models based on the data.

We have a wide variety of use cases, but we generally use it to have one place where we can store data. We also connect enterprise legacy systems such as SAP systems. I'm mostly connecting shop floor assets and the industrial machinery to store relevant data in one place and make it available. Via Athena, combined data can then be retrieved with easy SQL queries.

Usually, we use the newest version of the solution. If there are new updates available, we try to take them directly.

Our customers usually have a hybrid cloud or full cloud architecture with minimum on-premises data centers.

We have five to ten customer projects per year in our department in which we set up data lake houses using Redshift. Those companies have between 3,000 and 15,000 people. Not everybody has to use Redshift. Depending on the project and the size of the consulted company, there are 2,000 to 3,000 end users who need access to the stored data.

We are a consultancy company of 1,500 people. Those who use a sophisticated data warehouse architecture tailored to the business requirements, profit a lot since this is the foundation of business intelligence which makes a company more resilient and adaptable to change. We try to select the correct service for our customers, and when using AWS, we usually do data warehousing with Redshift.

The main benefit is that our portal for end users is running in AWS, so we can easily connect it to other AWS services. The most important part for us is the connectivity inside AWS.

Pricing sometimes depends on the setup (key, etc.) which makes it hard for somebody new to AWS. Detailed research has to be conducted to end up with a competitive solution in terms of pricing and performance.

I have used Redshift for a year and a half.

I would rate the stability as nine out of ten since AWS provides a relatively resilient solution in general due to load balancing and auto scaling capabilities.

I would rate the scalability as ten out of ten.

We are a certified partner of AWS, so we have a different level of customer service, but usually, the customer service is fast and efficient even without a direct contact person.

Positive

My company has more than 20 years of experience with SAP in general and the Data Warehouse Cloud is also used for customer projects.

I'm familiar with AWS in general, so the setup is not difficult. For people who struggle with IAM or role-based management, the setup can be challenging.

In cases where there is deep knowledge of creating a microservices architecture using AWS, a project can be executed quite fast. The only reason for using AWS is the aim for AWS Redshift as a data warehouse, the setup is not as easy.

The amount of time it takes to deploy depends on the project. We try to do our projects incrementally, so we start with one department and deploy all of the relevant data from that. It takes about two to three months to draft a minimum viable product, connect a couple of machines, and use Redshift to work with data consumed by analysis tools or reporting capabilities. It is possible to set up a data warehouse in one or two weeks.

There are different types of projects, and if we do it within a waterfall approach, it can take a long time.

When creating a data warehouse using Redshift, we first analyze the current situation and then sketch the architecture in AWS with the required ETL process to ingest and consume data, including the right processing to aggregate and enrich data according to business requirements. This is where all the customization comes in.

For deployment and maintenance for only one customer, I would say that three to six experienced people can handle the job. For maintenance work, in my opinion, there is only one person required for two or three hours a week maximum.

I would rate the setup as eight out of ten.

The cost will depend on how you set up your warehouse and what kind of data you store. I would rate the pricing as six out of ten.

I would rate this solution as nine out of ten.

My advice is to use a consultant to set it up correctly and to get familiar with data warehousing in general. It's pretty straightforward once you're using the data management console. Anyone could use Google to set it up, but to do it correctly and sketch the perfect architecture, an external consultancy company should handle it because the usual IT departments aren't that experienced.

I think Redshift is worth the money, and there's always some return. Most companies don't have a full solution for business intelligence. AWS provides a pretty good service architecture to implement this. Anyone who uses Redshift or a data warehouse in general will have a return on investment.

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 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.

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.

I have used DBeaver to connect to SAP Amazon Redshift and my main use case is for SQL. I map data from a graph database into SQL.

Amazon Redshift has benefited the company by allowing us to scale. 

The solution could improve in handling more data formats and more native support for RDF.

I have been using Amazon Redshift for approximately one and a half years.

The stability of Amazon Redshift is good.

Amazon Redshift is a scalability solution.

I have not used technical support. If I need support I ask my colleagues.

The initial implementation of Amazon Redshift is simple. There was not anything difficult about getting started with the solution.

It's important to have a grasp of the environment you're working in, including a basic understanding of Amazon AWS. Additionally, when selecting an interface to work with, it's essential to choose wisely, as you'll likely be using it for an extended period of time.

I rate Amazon Redshift an eight 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.