The Amazon S3 Glacier storage classes are purpose-built for data archiving, providing a low cost archive storage in the cloud. According to AWS, S3 Glacier storage classes provide virtually unlimited scalability and are designed for 99.999999999% (11 nines) of data durability, and they provide fast access to archive data and low cost.
$0
Per GB Per Month
Amazon Redshift
Score 8.9 out of 10
N/A
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
If your organization has a lot of archival data that it needs to be backed up for safekeeping, where it won't be touched except in a dire emergency, Amazon Glacier is perfect. In our case, we had a client that generates many TB of video and photo data at annual events and wanted to retain ALL of it, pre- and post- edit for potential use in a future museum. Using the Snowball device, we were able to move hundreds of TB of existing media data that was previously housed on multiple Thunderbolt drives, external RAIDs, etc, in an organized manner, to Amazon Glacier. Then, we were able to setup CloudBerry Backup on their production computers to continually backup any new media that they generated during their annual events.
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 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
Since the rest of our infrastructure is in Amazon AWS, coding for sending data to Glacier just makes sense. The others are great as well, for their specific needs and uses, but having *another* third-party software to manage, be billed for, and learn/utilize can be costly in money and time.
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.
We seldom need to access our data in Glacier; this means that it is a fraction of the cost of S3, including the infrequent-access storage class.
Transitioning data to Glacier is managed by AWS. We don't need our engineers to build or maintain log pipelines.
Configuring lifecycle policies for S3 and Glacier is simple; it takes our engineers very little time, and there is little risk of errant configuration.
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.
