Nutanix also provides similar products. It also offers different options for cloud providers.

One reason we chose this solution is that we are in the process of moving everything to the cloud. But that's not the only reason. My company wanted to consolidate everything into one system, and AWS provided all the necessary information in one place. For example, Lambda is for specific storage and limited membership; all this information is available in one cloud network. This way, data segregation is much faster and easier to use. It's just everything in one cloud network, so we decided to use it.

We also tried using Athena. However, Redshift was faster.

We are switching to Azure, although not because of the product or the services that we did not like. It's about AWS being competitors for logistic companies that we are working with. Also for security reasons, we do not know how secure the data is on the cloud.

If you are competitors then you don't know if the data can be accessed by your competitor, and the team can be looking at a demographic, which could impact your sales.

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.

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 evaluated Cloudera and Azure.

We have also tried Cosmos DB and Azure for our customers, but they were not chosen after the PoC. Azure and AWS have different database offerings and we experimented with both, but the customer ended up in production and we stayed with that.

We pretty much went with Redshift, as the company migrated everything to AWS. We might have looked at other database options, but we did not put much time into it.

I started using Amazon Redshift because I started working for this company that was working with both Azure and Amazon. The company eventually moved all to Amazon. I wasn't sure why they didn't continue to use Azure. My experience was more with Microsoft technology so I prefer Azure. But, there are some interesting features in Amazon Redshift that works better. I have also used Oracle Cloud.

I evaluated Hadoop and Spark, along with Redshift. I have no negative comments about those other products. Redshift is flexible in terms of configuration, maintenance and security, especially VPC configuration, which secures our data a lot.

For setting up a data warehouse, BigQuery was a serious contender. BigQuery is simpler to setup and scale. It's also more of a black box: you worry less what's inside and how it scales and you get charged for what you consume (which is both a pro and a con). With Redshift, you choose in advance the type of machine you want, like EC2 (resizing your cluster is easy).

We also looked at the Oracle solution.

We did not evaluate other options prior to selecting this solution.

We analyzed Microsoft, Oracle, AWS RDS and Mango DB for our requirements.

I only provided advice to my customers, but some looked at Azure SQL DW , Greenplum, Netezza, and Google Big Query as possible alternatives

I did not. we did consider using BigQuery Data Analytics, but eventually, we decided to use Redshift.

We have used different options over the last 20 years. We found AWS Redshift to be the leader in capability and provides an ecosystem of related services from AWS, many of which are free.

We were thinking about using a self-managed PostgreSQL. We chose Redshift because we didn't need to manage it ourselves and because it integrates with the rest of the AWS services more fluently.

We are currently evaluating Druid.