Amazon DocumentDB (with MongoDB compatibility) is presented by the vendor as a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. As a document database, Amazon DocumentDB is designed to make it easy to store, query, and index JSON data.
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Amazon Redshift
Score 8.9 out of 10
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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
AWS Document DB (with MongoDB compatibility) is well suited when for all the workloads due to its huge feature offerings which will reduce our operational overhead and due to that we can focus more on our WorkLoad rather than optimising and fine tuning Databases. Its Offerings are Advanced Monitoring, DB cluster Upgrades, Migration Assistant, High Availability, Fault Tolerance, Data Durability, Security, Storage Auto Scaling, Backup Restore policies.AWS Document DB (with MongoDB compatibility) some of the features that are there in some other services like MongoDB Atlas that offers vast amount of features plus Supports Multi Cloud while Deploying Database clusters, Immediate support to latest Mongo DB versions, Mobile & Edge Sync like Atlas Edge Sync, Freedom to choose Database deployment in Any top Public Cloud, Having more then 100 plus Monitoring and Telemetry metrics for index and schema recommendations, More Compatibility with MongoDB queries.
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 DocumentDB (with MongoDB compatibility) provides Auto scaling of cluster as a by default functionality through this we can focus on more on our applications end
Through AWS Document DB without much operation overhead we can configure for Database's high availability, Durability, Backup Restores policies, Advanced Monitoring, Security Parameters.
Also they can provide us a Guide for Database Migration from any Supported Mongo DB vendor to AWS Document DB.
Via AWS Document DB query Logging ( Profiling ) we can fine tune our database queries and hence improving our END to END Customer Experience and Product Enhancements.
[Amazon] Redshift has Distribution Keys. If you correctly define them on your tables, it improves Query performance. For instance, we can define Mapping/Meta-data tables with Distribution-All Key, so that it gets replicated across all the nodes, for fast joins and fast query results.
[Amazon] Redshift has Sort Keys. If you correctly define them on your tables along with above Distribution Keys, it further improves your Query performance. It also has Composite Sort Keys and Interleaved Sort Keys, to support various use cases
[Amazon] Redshift is forked out of PostgreSQL DB, and then AWS added "MPP" (Massively Parallel Processing) and "Column Oriented" concepts to it, to make it a powerful data store.
[Amazon] Redshift has "Analyze" operation that could be performed on tables, which will update the stats of the table in leader node. This is sort of a ledger about which data is stored in which node and which partition with in a node. Up to date stats improves Query performance.
We've experienced some problems with hanging queries on Redshift Spectrum/external tables. We've had to roll back to and old version of Redshift while we wait for AWS to provide a patch.
Redshift's dialect is most similar to that of PostgreSQL 8. It lacks many modern features and data types.
Constraints are not enforced. We must rely on other means to verify the integrity of transformed tables.
Just very happy with the product, it fits our needs perfectly. Amazon pioneered the cloud and we have had a positive experience using RedShift. Really cool to be able to see your data housed and to be able to query and perform administrative tasks with ease.
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
Than Vertica: Redshift is cheaper and AWS integrated (which was a plus because the whole company was on AWS). Than BigQuery: Redshift has a standard SQL interface, though recently I heard good things about BigQuery and would try it out again. Than Hive: Hive is great if you are in the PB+ range, but latencies tend to be much slower than Redshift and it is not suited for ad-hoc applications.
Redshift is relatively cheaper tool but since the pricing is dynamic, there is always a risk of exceeding the cost. Since most of our team is using it as self serve and there is no continuous tracking by a dedicated team, it really needs time & effort on analyst's side to know how much it is going to cost.
Our company is moving to the AWS infrastructure, and in this context moving the warehouse environments to Redshift sounds logical regardless of the cost.
Development organizations have to operate in the Dev/Ops mode where they build and support their apps at the same time.
Hard to estimate the overall ROI of moving to Redshift from my position. However, running Redshift seems to be inexpensive compared to all the licensing and hardware costs we had on our RDBMS platform before Redshift.
