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 have also used Microsoft SQL server and Oracle.

Previously, we tried the Snowflake database, which works really well. The expectations were really good with the performance, also the DDL, DML operations on the processing of the data.

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.

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.

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

I have previously used ClickHouse.

We were using an on-premise MySQL data warehouse. To reduce the cost and improve scalability, we switched to a cloud version of data warehouse databases.

We used ODBC before to maintain our tables. Oracle ODBC was used as a server, as a data warehouse environment.

Changing to Redshift was a big change for us. However, after a while, we get used to it and it is okay now as it's coming together under a bigger picture of a framework, as a bigger framework. We are going to keep everything in the cloud and moving to AWS and we will put everything there and manage it as a framework going forward.

Prior to going with Amazon Redshift, I used Azure Data Warehouse and Google BigQuery.

The decision to go with Amazon Redshift was reached by the CTO, so that, in respect of the data warehouse, the entire environment is set up in Amazon.

I feel Amazon to be better than the other solutions we worked with.

This was our first foray into a data warehouse and customer facing BI.

In the past year, we have used Azure, but the committee has chosen Amazon Redshift because it is better than Azure for our company needs. We have grown around Amazon Redshift and other AWS solutions.

Previously, we were using Snowflake in our company. We switched since Amazon Redshift was cheaper. Now, I use Snowflake and Amazon Redshift.

I have also used Snowflake and its methods for ingesting real-time data are faster. It also offers a bit more functionality and a bit more flexibility. It's a bit easier to maintain and faster to scale, but more expensive as well.

To me, the big drawback with Snowflake is that the data is not stored in your AWS or Azure subscription, or AWS account. They store the data in their own account that they manage for you, which might be a problem for some companies in terms of compliance and legal requirements.

Azure Synapse and Google BigQuery are also competing solutions.

We have worked with Teradata and more recently, have been working with Azure SQL Warehouse. Teradata is an on-premises solution and the upfront costs are high. Comparing Azure SQL Warehouse and Amazon Redshift, in terms of features I think that they are pretty much on par.

The SQL Data Warehouse does have better OLAP capabilities, and they also offer a level of serverless capability where they have split the compute and the storage. This means that they can operate at a lower cost in the development environment.

Many of our clients have begun to adopt Power BI, and once they start using it, they tend to lean towards Azure and the Azure SQL Data Warehouse. The fact that Power BI is free, makes quite an impact.

I have experience working in Hadoop as well. When I compare the two (Redshift & Hadoop), Redshift is more user friendly in terms of configuration and maintenance.

At my previous company, we switched from MongoDB to Redshift. The main reason was price and performance. At my current company, we started a data warehouse (greenfield project). The choice was between Google BigQuery and AWS Redshift. The main criteria was that Redshift was PostgreSQL-based and supports CTE and Window functions (PostgreSQL features).

Initially, we were using the Microsoft SQL solution. We decided to move over to this product due to the DWH volume and performance.

We have not used any other solution.

I worked on Teradata and IBM solutions. Redshift gives performance similar to these solutions and costs a fraction of the amount.

I’ve helped customers migrate off Teradata, SQL Server , Oracle Exadata, Greenplum, and ParAccel Matrix to Redshift. Some due to cost savings, others because of the EOL of the product.

I was on a team that was using AWS tools for Dick Smith Electronics (now liquidated). The tools ceased use in February of 2016.

Prior to that, we were using them fully for about 3 years. We loaded data to Redshift according to the best practices included in the online docs and through consultation with the AWS staff. The combination of S3 and Redshift for this purpose was very high in performance. Redshift was used to provide the data model to an instance of MicroStrategy for BI reporting.

We were using MicroStrategy, which generated all the SQL that our reporting services needed.

As such, I could only comment on the data engineering phase. Technically, this was so impressive that I don’t know what to add. I don’t recall feeling that it missed anything. If anything, I was not using all the available features. AWS documentation is great in this regard. You can tell they have put a lot of thought into it.

A lot of the future direction in database technology has to do with memory optimization and concurrency (VoltDB). This is more targeted towards transactional processing, and not data warehousing.

Memory-only data warehousing solves a lot of access issues without having to think too hard about the problem from the consumers' point of view. I am sure that you can already configure this.

We tried prior solutions, but they had limited or no scalability/agility.

We did not have a previous solution. Redshift worked for us the first time we tried. The pricing could not be beaten by anything else in the market at that time.

The company was using other solutions such as Google Cloud and the Microsoft Cloud Service, but I have not personally used these solutions.

Previously, we were using AWS RDS for our use case. We found that we had outgrown it. Our data grew in size and we wanted to still have performance queries.