Db2 vs. Google BigQuery vs. PostgreSQL

I have primarily used it as the basis for a SIS - but I have migrated more than a few systems from there database systems to DB2 (Filemaker, MySQL, etc.). DB2 does have a better structural approach, as opposed to Filemaker, which allows for more data consistency, but this can also lead to an inflexibility that can sometimes be counterintuitive when attempting to compensate for the flexibility of the work environment as Schools tend to have an all in one approach.

Incentivized

Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.

Incentivized

Based on my experience, PostgreSQL is exactly what I imagine when thinking of a relational database: it is fast for querying and intuitive (if the underlying relations have been well written) and its writes are also efficient. Of course you need to know that a relational database is what you need, for example I would not recommend it if you have a lot of unstructured data that have no way to create a relation from and if you need to run heavy aggregations over your data, as it is not where PostgreSQL would shine.

Incentivized

• DB2 maintains itself very well. The Task Scheduler component of DB2 allows for statistics gathering and reorganization of indexes and tables without user interaction or without specific knowledge of cron or Windows Task Scheduler / Scheduled jobs.
• Its use of ASYNC, NEARSYNC, and SYNC HADR (High Availability Disaster Recovery ) models gives you a range of options for maintaining a very high uptime ratio. Failover from PRIMARY to SECONDARY becomes very easy with just a single command or windowed mouse click.
• Task Scheduler ( DB2 9.7 and earlier ) allows for jobs to be run within other jobs, and exit and error codes can define what other jobs are run. This allows for ease of maintenance without third party softwares.
• Tablespace usage and automatic storage help keep your data segmented while at rest, making partitioning easier.
• Ability to run commands via CLI (Command Line Interface) or via Control Center / Data Studio ( DB2 10.x+) makes administration a breeze.

Incentivized

• Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
• Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
• Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.

Incentivized

• 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.

Incentivized

• everything about Db2 is 'heavy', even the Community edition and some of the clients
• difficult to configure for beginners /option overload
• LUW and z platforms have minor inconsistencies that really should be a non-issue
• support seems to be declining in terms of quality/quickness of responses

Incentivized

• It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
• The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.

Incentivized

• 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.

Incentivized

The DB2 database is a solid option for our school. We have been on this journey now for 3-4 years so we are still adapting to what it can do. We will renew our use of DB2 because we don’t see. Major need to change. Also, changing a main database in a school environment is a major project, so we’ll avoid that if possible.

Incentivized

We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.

Incentivized

As a needed software for day to day development activities

Incentivized

You have to be well versed in using the technology, not only from a GUI interface but from a command line interface to successfully use this software to its fullest.

Incentivized

web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2

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.

Incentivized

I have never had DB2 go down unexpectedly. It just works solidly every day. When I look at the logs, sometimes DB2 has figured out there was a need to build an index. Instead of waiting for me to do it, the database automatically created the index for me. At my current company, we have had zero issues for the past 8 years. We have upgrade the server 3 times and upgraded the OS each time and the only thing we saw was that DB2 got better and faster. It is simply amazing.

Incentivized

I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.

Incentivized

PostgreSQL's availability is top notch. Apart from connection time-out for an idle user, the database is super reliable.

Incentivized

The performances are exceptional if you take care to maintain the database. It is a very powerful tool and at the same time very easy to use. In our installation, we expect a DB machine on the mainframe with access to the database through ODBC connectors directly from branch servers, with fabulous end users experience.

Incentivized

I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.

Incentivized

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.

Incentivized

Easily the best product support team. :) Whenever we have questions, they have answered those in a timely manner and we like how they go above and beyond to help.

Incentivized

BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.

Incentivized

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.

Incentivized

the material was very clear and all subjects have been handled

Incentivized

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.

Incentivized

db2 work well with the application, also the replication tool can keep it up

The online documentation of the PostgreSQL product is elaborate and takes users step by step.

Incentivized

With the other two mentioned above, I needed to have processes and frameworks that executed outside of the environment driving DB management operations. Yes, these are completely different solutions; however, the support you get for framework, library, and language support allows for runtime at a different layer than with other solutions.

Incentivized

Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.

Incentivized

In my experience using all of these products over many years, PostgreSQL is better than any of them in reliability, performance, productivity, cost, scalability and interoperability across operating systems.

Incentivized

DB2 can be configured and can work with a variety of applications as opposed to how it was designed initially to only with with IBM mainframes. It's easy implementation process makes it a good buy for many organizations to scale their applications to be the best in terms of versatility, resilience and application performance

Incentivized

We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.

Incentivized

The DB is reliable, scalable, easy to use and resolves most DB needs

Incentivized

• Time efficiency is the major point to highlight, especially the minimal time this takes while querying a larger set of data.
• Reliability becomes another major factor as the application can be accessed at any point in time.
• Users might initially find it challenging to use the application.

Incentivized

• In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
• Scalability is up-to-date and really helpful in multiple places.
• Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
• Also, it gives us more insights from our data, helping us make smarter decisions for our business.

Incentivized

• A robust and reliable platform reduces the risk of data loss and confusion.
• Using an almost free product is economical. No license fees = minimal ownership cost.
• Efficiency is high due to quickness and excellent resource management.
• PostgreSQL helps us achieve our goals, and there are no time-consuming management tasks.