Amazon Redshift

It is well suited in scenarios where you have distributed data sources and would like to build an ETL pipeline with limited data engineering efforts. Operations time and cost is relatively low compared to other tools. Also it offers great connectivity with Heap with no technical know-how required. It is mostly self managed and reliable.

[Amazon] Redshift is suited for various use cases like Time series data, Structured / relational data, Semi structured data like JSON, etc.

[Amazon] Redshift might not work 100% well with full performance, for Graph DB use cases.

Amazon redshift is best suited for data analysis and is not suited for transactions.
for eg. you can use amazon redshift to gain insights from a large data set but cannot use it to do a transaction level update and insert

If you are looking for something easy to implement that will give you a nice performance, I would suggest Amazon Redshift. I'm using it in AWS environment, so I don't know if in another cloud environment the performance and all the features would be nice as well. It's also important to check if the price fits to you too.

If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for).

Amazon Redshift performs extremely well for reporting/analytics data and is way ahead of other competitors. The biggest challenge is migrating data from on-premises databases to Amazon Redshift. The initial hurdle is a major one.

It's appropriate for ad hoc queries on semiorganized data.

Redshift is great data warehouse solution if you have several billion rows tables. More than 200 very important improvements were added in several years' time. With new Redshift instance types solution has separation of storage and compute and magnitude better query response times. Don't use Redshift if you have less than several billion rows tables.

Redshift is best suited for our data storage and designing our fact and dimension tables. We keep our non-structured data there that can be accessed at any time as well as our relational database. I'd say that if you don't have a need for a relational databasae, then Redshift probably isn't going to be a viable product.

If you do not require a fast I/O rate. And frankly you should not expect that from data warehouse. And if you do not mind that your data is on cloud and only on AWS, and if you want a scalable and fast implementation of data lake, then you should consider Amazon Redshift.

Except for the price, Amazon Redshift is a great tool and has the fastest performance across all the data warehouses we have seen. It's easy to connect with Talend, which makes it a better option to use. I like the UI better than most of the other DW. Overall it's a great DW tool.

Redshift is appropriate when the number of concurrent users are low and pointed queries are the focus. It is not appropriate when a large number of concurrent users is to be supported,

It's very cost-effective from other databases we were using for our data warehouse. It was really easy to set up and it used our ETL tools to migrate data from different data sources. We added functionality add aggregate the data set for our Analytics team to analyze.

Amazon Redshift is well suited for fetching data from multiple different sources and servers, and it is very easy to learn how to use this tool. It is less appropriate for situations where you may need to process only limited rows, as this takes a large amount of time since you cannot create multiple tables in it.

Amazon Redshift is the data warehouse under the umbrella of AWS services, so if your application is functioning under the AWS, Redshift is the best solution for this. For large amounts of data, the application is the best fit for real-time insight from the data and added decision capability for growing businesses. If your application is outside of AWS it might add more time in data management.

I recommend all to use Redshift, It is easy to use and maintain. We have reduced the number of Salesforce licenses due to real-time data we have in Redshift. People are happy to use Redshift.

Use Redshift for data warehouses, especially if your data is already in the cloud (AWS). It's great for large datasets, and fast too, even if your dataset is column heavy. It's less so for when you have a bunch of rows. All in all, it's a good starting point for any aspiring data warehouse, but there are other promising solutions too. E.g. Snowflake.

Amazon Redshift is great for analytics, reporting, and complex queries for statistical modeling and machine learning. Its ability to run parallel queries in a simple SQL environment makes the transition from traditional DB very easy. Very good for loading/reading/writing large datasets. I would not recommend RedShift for an environment that requires single row reads/updates, which it is not optimized for.

Amazon Redshift is good for when you need a Data Warehouse solution or a user-experience such as SQL kind queries. It is also good if you have limited budget constraints. It's not suggested when someone has DML queries such as INSERT, UPDATE, DELETE.

[It's] very good for reports and dashboards and is easy to use and fast performance makes it a good choice. It excels in columnar architecture and aggregates. If you have many clients as we do, you need to have separate schemas for each of them. That is a good way but also there is too much clutter spread all over. That is the only drawback I see, end users can see data in the schema, there are no individual permissions allotted.

Very well suited for aggregating/denormalizing data when you need a reporting environment. Can provide extremely fast querying for analytical purposes. Very nice to not have to have in house responsibility for sensitive data.

Not appropriate for a transactional system (though this is not what it is built for obviously). Must keep in mind the data you are syncing up to the cloud and scrub if necessary before. Something to always be mindful of of course.

It is best for large structured data sets with data normally loaded from files, instead of through heavy SQL data manipulation. If there are SQL data manipulation, better to have mainly DDL (create/drop/alter) instead of DML (insert/update/delete) to achieve the same goals. It is also better for smaller number of concurrent connections (less than 500).

It is well suited for:

• Petabytes of data requiring near real-time analysis
• Massive Data Insertions
• Massive Data Reads

It isn't well suited for:

• Web apps
• Smaller transactional inserts
• Smaller reads

You wouldn't drive an 18-wheeler to the corner store to pick up a bag of chips. Your specific need will determine whether or not Redshift is suited for the job.

Redshift is ideal for small teams. It is fully managed. CloudWatch metrics are provided out-of-the-box, and it integrates well with other AWS products, such as DMS. The Redshift console is among the better AWS consoles. Redshift offers adequate performance. Spectrum offers a convenient way to access our data lake, but we have encountered issues with recent versions.