Amazon Redshift

Amazon Redshifts has fewer features but at the same time, you also have some gains once it is running on AWS Cloud and it is really easy to set up. Besides that, in our case, it is a bit cheaper and we don't really need the extra features that you can find on Snowflake. Another thing that looks easier for us is the way to scale the instance.

Amazon Redshift, BigQuery, and Snowflake are all fully managed data warehouse services that are designed to handle large volumes of structured data and support business intelligence and analytics efforts. However, Amazon Redshift has the upper hand with its cost-effective storage, granular control, full integration with AWS and its design allowing it to scale up to petabyte-scale data warehouses.

Biggest advantage of Amazon Redshift is it's part of the aws ecosystem. When tuned well it is also very cheap compared to something like Snowflake. And compared to spark or databricks, Amazon Redshift is a solid warehouse that's well suited for tabular data. We use it for user logs, which is a very common usecase.

We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014.
Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure.
Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a data store.
[Amazon] Redshift was ideal with the price structure, performance and ROI[.]

Redshift is better cost wise and also since the whole ecosystem is set in AWS, it is wise to use redshift

The best advantage for us was the easy way to integrate our current solution in AWS to Amazon Redshift.

Amazon Redshift is one of the fastest service offerings available in the market now. Plus you get an advantage of using a cutting edge compute service offering from AWS. Other technologies are fast but not as good as Amazon Redshift, I would say. Our business is interested in exploring technologies offered by a company in the form of stack unlike others which only concentrate on offering one of the services.

Redshift leapfrogged Hive back when Hive was trying to figure out how to implement indexes, providing a more stable, standardized (postgres), easy to use (any postgres client), easier to administer, and scalable solution for querying server logs and raw usage data.

Now, Snowflake has surpassed Redshift in usability and scalability.

1. Redshift has better compression (automated) consuming less space then competitors
2. Automated Vacuum Delete for having consistent performance
3. AWS introduced ra3 node types for simple separation of compute and storage
4. Redshift has automatic concurrency scaling, provisioning new nodes if necessary
5. Better and more native integration with s3 data lake, possible destination for AWS Kinesis Firehose
6. Transparent 'pay-as-you-go' commercial model
7. Automatic backup

Its definitely an improvement on all fronts for our business needs. Again, our MySQL server was really slow and we needed a more efficient solution. It was a major upgrade, but it is much more expensive than an in house server. It was expected but I'd say that lots of headaches dealing with server slowness have been relieved.

Amazon Redshift supports multiple data formats including multiple structured data formats. And it is easy to implement a cluster if you do not have knowledge of data lake solution. Also when you do not need a lot of resources, you can just scale down so you do not have to spend more than you need.

Amazon Redshift has a better UI, hands down. And it is easy to integrate with bigger tools like Talend. It has many issues when it comes to understanding the architect perspective like Toad, which has a better UI for architect data together. However, that is because we are not used to many features of Amazon Redshift. I like how they don't change the UI often like Azure, making it harder to adjust to the new tool look and feel.

We like Snowflake for its separation of computing and storage and also the separation of data warehouse different users. We replaced Redshift with Snowflake. However, Snowflake is great for its pay for performance kind of methodology.

The main reason we chose Redshift was because of the cost-effectiveness of running and maintaining the warehouse.

It works on the cloud and we use the platform Dbeaver which is very unique and easy to maintain. There are very limited tools of this kind but the security issues are pretty high within those tools.

As our applications are hosted on AWS service, Redshift is the best option for us. Also, it provide a near to real-time performance on limited datasets and less complex queries. High availability is the major concern for any growing business and AWS is the best option for this. Compared to other services like Google and Azure, this one has limited functionality but great support by the AWS customer service.

No comment on this.

We are currently on Redshift, because it was out before Snowflake. However, Snowflake looks promising. It's the new shiny toy that gives options that Redshift does not provide for. The big thing is that storage and compute can be scaled separately, whereas you cannot do that in Redshift. That's a big one. If you have enough data, it's worth it and should be cheaper.

Most of our stack is on AWS, so while Snowflake and BigQuery was a viable option from a performance perspective, it was easier to integrate with RedShift. We considered hosting SQL Server on AWS or using Amazon RDS (Postgres or MySQL), however, the self-service aspect of RedShift and its superior parallel query execution made us choose RedShift.

Snowflake supports semi-structured data types and provided solutions to manage/process the semi-structured data. It supported sharing data between the different accounts and makes it easy in the scale and scale down process. Snowflake doesn't limit users on the database.

Amazon Redshift is much easier to set up and start using. It interacts well with the PostgreSQL client (psql) and shares certain basic data dictionary, and people familiar with PostgreSQL feel right at home. The cluster is part of AWS services offering, and it works well with other services such as S3 and EC2.

Some organizations use PostgreSQL as an OLAP store. PostgreSQL offers a modern SQL dialect, data types, and features that Redshift lacks. RDS is a great managed PostgreSQL product. However, PostgreSQL is a poor choice for a data warehouse. It's row-oriented storage requires careful schema and index design to ensure analytical queries perform adequately.

Prezi is using AWS so Amazon Redshift was the obvious choice. It is fast, scalable and easy to use. Supplemented with Spark and Hive I'm completely satisfied using Redhsift. Sometimes I miss commands I used earlier on MS and Oracle SQL and the lack of procedural features is also negative.