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
PostgreSQL
Score 8.7 out of 10
N/A
PostgreSQL (alternately Postgres) is a free and open source object-relational database system boasting over 30 years of active development, reliability, feature robustness, and performance. It supports SQL and is designed to support various workloads flexibly.
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.
PostgreSQL, unlike other databases, is user-friendly and uses an open-source database. Ideal for relational databases, they can be accessed when speed and efficiency are required. It enables high-availability and disaster recovery replication from instance to instance. PostgreSQL can store data in a JSON format, including hashes, keys, and values. Multi-platform compatibility is also a big selling point. We could, however, use all the DBMS’s cores. While it works well in fast environments, it can be problematic in slower ones or cause multiple master replication.
The stability it offers, its speed of response and its resource management is excellent even in complex database environments and with low-resource machines.
The large amount of resources it has in addition to the many own and third-party tools that are compatible that make productivity greatly increase.
The adaptability in various environments, whether distributed or not, [is a] complete set of configuration options which allows to greatly customize the work configuration according to the needs that are required.
The excellent handling of referential and transactional integrity, its internal security scheme, the ease with which we can create backups are some of the strengths that can be mentioned.
The query syntax for JSON fields is unwieldy when you start getting into complex queries with many joins.
I wish there was a distinction (a flag) you could set for automated scripts vs working in the psql CLI, which would provide an 'Are you sure you want to do X?' type prompt if your query is likely to affect more than a certain number of rows. Especially on updates/deletes. Setting the flag in the headless(scripted) flow would disable the prompt.
Better documentation around JSON and Array aggregation, with more examples of how the data is transformed.
Postgresql is the best tool out there for relational data so I have to give it a high rating when it comes to analytics, data availability and consistency, so on and so forth. SQL is also a relatively consistent language so when it comes to building new tables and loading data in from the OLTP database, there are enough tools where we can perform ETL on a scalable basis.
The data queries are relatively quick for a small to medium sized table. With complex joins, and a wide and deep table however, the performance of the query has room for improvement.
There are several companies that you can contract for technical support, like EnterpriseDB or Percona, both first level in expertise and commitment to the software.
But we do not have contracts with them, we have done all the way from googling to forums, and never have a problem that we cannot resolve or pass around. And for dozens of projects and more than 15 years now.
The online training is request based. Had there been recorded videos available online for potential users to benefit from, I could have rated it higher. The online documentation however is very helpful. The online documentation PDF is downloadable and allows users to pace their own learning. With examples and code snippets, the documentation is great starting point.
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.
Postgres stacks up just [fine] along the other big players in the RDBMS world. It's very popular for a reason. It's very close to MySQL in terms of cost and features - I'd pick either solution and be just as happy. Compared to Oracle it is a MUCH cheaper solution that is just as usable.
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.
The user-role system has saved us tons of time and thus money. As I mentioned in the "Use Case" section, Postgres is not only used by engineering but also finance to measure how much to charge customers and customer support to debug customer issues. Sure, it's not easy for non-technical employees to psql in and view raw tables, but it has saved engineering hundreds of man-hours that would have had to be spent on building equivalent tools to serve finance or customer support.
It provides incredibly trustworthy storage for wherever customer data dumped in. In our 6 years of Postgres existence, we have not lost a byte of customer data due to Postgres messing up a transaction or during the multiple times the hard-drives failed (thanks to ACID compliance!).
This is less significant, but Postgres is also quite easy to manage (unless you are going above and beyond to squeeze out every last bit of performance). There's not much to configure, and the out of the box settings are quite sane. That has saved us engineers lots of time that would have gone into Postgres administration.
