DB2 is a family of relational database software solutions offered by IBM. It includes standard Db2 and Db2 Warehouse editions, either deployable on-cloud, or on-premise.
$0
Google BigQuery
Score 8.8 out of 10
N/A
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
MySQL
Score 8.3 out of 10
N/A
MySQL is a popular open-source relational and embedded database, now owned by Oracle.
N/A
Pricing
Db2
Google BigQuery
MySQL
Editions & Modules
Db2 on Cloud Lite
$0
Db2 on Cloud Standard
$99
per month
Db2 Warehouse on Cloud Flex One
$898
per month
Db2 on Cloud Enterprise
$946
per month
Db2 Warehouse on Cloud Flex for AWS
2,957
per month
Db2 Warehouse on Cloud Flex
$3,451
per month
Db2 Warehouse on Cloud Flex Performance
13,651
per month
Db2 Warehouse on Cloud Flex Performance for AWS
13,651
per month
Db2 Standard Edition
Contact Sales
Db2 Advanced Edition
Contact Sales
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
No answers on this topic
Offerings
Pricing Offerings
Db2
Google BigQuery
MySQL
Free Trial
Yes
Yes
No
Free/Freemium Version
Yes
Yes
No
Premium Consulting/Integration Services
Yes
No
No
Entry-level Setup Fee
Optional
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Db2
Google BigQuery
MySQL
Considered Multiple Products
Db2
Verified User
Analyst
Chose Db2
MySQL was definitely faster in terms of making queries, but DB2 had many features that protected against errors and easier to use for SQL beginners.
It's almost not comparable because they all do the same job in varying degrees. There are some things I like about Db2 that I don't enjoy about Oracle, but it mostly comes down to how it works and where it stores everything like SYS tables in Db2. MySQL is probably the fastest …
Before selecting Db2, I had the opportunity to work with three different products: MySQL, IBM API Connect and IBM Cloud Databases.MySQL is a very popular and effective relational database management system, especially known for its ease of use and reliability. While working …
I have experience with the above-mentioned similar products but mainly with MySQL. In terms of speed and query optimization capabilities, Db2 is far ahead in comparison to MySQL. Because of various issues like scalability, multiple departments hitting DB together causing …
It is faster and the transactions are much more safer and reliable if I compare it with the two SQL database I mentioned above, as far as MongoDB is concerned it completely depends upon the requirement of the project, if a SQL or a NoSQL database is more suitable for a project.
From working with other databases, I always felt that Db2 was at the top of its game in all aspects of performance, recoverability, and stability—pretty much everything you want out of an Enterprise database system.
IBM Db2 provides solutions for Data Lakes, Operational Databases, Data Warehouses, and Fast Data. IBM has a rich history of being a diversity, equity, and inclusion leader. Easy to design, implement, test, and implement with huge support material across different platforms. …
Considering Price, features configurations timelines of the IBM Db2 we found that is very Robust in Scalability, Reliability, Highly Available. also, we are already a IBM products user and we are much satisfied with the overall product as well as customer support from IBM team. …
Db2 is more scalable, reliable, and easily configurable than all the products that we evaluated. We were already using some of the services provided by IBM and were satisfied with the support and pricing. This led us to select Db2 as our database management system.
Tried tested true and dependable. Main distinguishing factor however is the ongoing time in which it has been relied on, the preference by some stakeholders for ensuring sensitive data security, and its flexibility
Db2 is one of the oldest and mature rdbms available in the market. IBM products were already been used in the organization. Cost effective in terms of licensing.
DB2 was more scalable and easily configurable than other products we evaluated and short listed in terms of functionality and pricing. IBM also had a good demo on premise and provided us a sandbox experience to test out and play with the product and DB2 at that time came out …
Db2 is one of the best relational databases I’ve used. It has the ability to maintain large amount of data and execution of million transactions in fraction of a second. If you use it properly, an organization can build a database with thousands of tables, and it can provide …
DB2 is much more robust than Oracle or mySQL when used in the Z/OS or Linux platform as it has the best error detection/warning system and also is very fast when accessed over the LAN in remote branch locations. It is scalable to a limited extent though as is the case in all …
Compared to PostgreSQL and MySQL, Google BigQuery is faster and more scalable for large datasets. It’s serverless, so there’s no need to manage infrastructure. We chose Google BigQuery for its ease of use built-in analytics features
Main reason is how it integrates directly with the google ecosystem which really facilitates the automatization proceses for the whole company. This ensures that sales and all the other departments have the correct information on a daily bases with a ease of use with day to day …
I have used most of the data analytics platforms. Based on my work, I have found that the user interface of Google BigQuery is simple to navigate. I like the front view - ease of joining tables, and integration with other platforms.
Postgres, SQL Server, DB2, Oracle, DashDB, MongoDB, RedShift - all of them have their strengths and weaknesses. I will say this about MySQL though, it is generally the first database chosen by a startup. It's easy to use, easy to deploy, free, and it just works.
I have used Google BigQuery and it is very difficult to start with it. Although it is very fast and the speed performance is much better with BigQuery but it costs and is very difficult to start with. There's also no proper documentation on it, so MySQL wins in terms of …
MySQL is perceived as less scalable than DB2. DB2 provides for an easy migration up to more scale if it is acceptable or required to remain in the IBM ecosystem, which can scale all the way to z-Series mainframes. For some enterprises like insurance and banking, this is a …
Before MySQL, our team was exploring and evaluating different options for a good RDMS (relational database management system) service. We explored Oracle, MSSQL, and Google BigQuery. Most of these are costly and not easy to maintain in the long run in terms of price especially …
We chose MySQL because of its open-source nature and its compatibility with various systems, languages, and databases. It is easy to use and fast. Additionally, it has been in the market for more than 30 years now which makes it a reliable option when compared to its …
Verified User
Consultant
Chose MySQL
it is cost effective solution and that time we were looking the good RDMS which can support the GIS based datatypes.
MySQL has most of the functionality of other, very costly, alternatives without the big price tag. It is open-source with improvements coming at a relatively good rate. It is not as robust as those other offerings and can have some challenging points at scale for large …
MySQL provides the option to reduce support and maintenance cost when P0 Level 1 support is not really needed for databases used for noncritical use cases and workloads. Other versions that include Microsoft SQL, Amazon RDS, etc don't provide such options and are overkill. …
The main reason that we went with MySQL is the cost. It's very cost effective and can do almost everything that Oracle can do. Database management is also very simple when compared to Oracle as we didn't have to contact the DBA for issues. Also, we found a lot of improvement in …
I have used more than 10 different SQL databases over the course of my career. Of those, the three I find myself using over and over include MySQL, Oracle and SQL Server. I have actually replaced smaller deployments of Oracle and SQL Server with MySQL as a way to reduce …
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.
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
MySQL is best suited for applications on platform like high-traffic content-driven websites, small-scale web apps, data warehouses which regards light analytical workloads. However its less suited for areas like enterprise data warehouse, OLAP cubes, large-scale reporting, applications requiring flexible or semi-structured data like event logging systems, product configurations, dynamic forms.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
Learning curve: is big. Newbies will face problems in understanding the platform initially. However, with plenty of online resources, one can easily find solutions to problems and learn on the go.
Backup and restore: MySQL is not very seamless. Although the data is never ruptured or missed, the process involved is not very much user-friendly. Maybe, a new command-line interface for only the backup-restore functionality shall be set up again to make this very important step much easier to perform and maintain.
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.
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.
For teaching Databases and SQL, I would definitely continue to use MySQL. It provides a good, solid foundation to learn about databases. Also to learn about the SQL language and how it works with the creation, insertion, deletion, updating, and manipulation of data, tables, and databases. This SQL language is a foundation and can be used to learn many other database related concepts.
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.
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
I give MySQL a 9/10 overall because I really like it but I feel like there are a lot of tech people who would hate it if I gave it a 10/10. I've never had any problems with it or reached any of its limitations but I know a few people who have so I can't give it a 10/10 based on those complaints.
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.
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.
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.
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.
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.
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.
We have never contacted MySQL enterprise support team for any issues related to MySQL. This is because we have been using primarily the MySQL Server community edition and have been using the MySQL support forums for any questions and practical guidance that we needed before and during the technical implementations. Overall, the support community has been very helpful and allowed us to make the most out of the community edition.
DB2 was more scalable and easily configurable than other products we evaluated and short listed in terms of functionality and pricing. IBM also had a good demo on premise and provided us a sandbox experience to test out and play with the product and DB2 at that time came out better than other similar products.
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
MongoDB has a dynamic schema for how data is stored in 'documents' whereas MySQL is more structured with tables, columns, and rows. MongoDB was built for high availability whereas MySQL can be a challenge when it comes to replication of the data and making everything redundant in the event of a DR or outage.
By using DB2 only to support my IzPCA activities, my knowledge here is somewhat limited.
Anyway, from what I was able to understand, DB2 is extremely scallable.
Maybe the information below could serve as an example of scalability.
Customer have an huge mainframe environment, 13x z15 CECs, around 80 LPARs, and maybe more than 50 Sysplexes (I am not totally sure about this last figure...)
Today we have 7 IzPCA databases, each one in a distinct Syplex.
Plans are underway to have, at the end, an small LPAR, with only one DB2 sub-system, and with only one database, then transmit the data from a lot of other LPARs, and then process all the data in this only one database.
The IzPCA collect process (read the data received, manipulate it, and insert rows in the tables) today is a huge process, demanding many elapsed hours, and lots of CPU.
Almost 100% of the tables are PBR type, insert jobs run in parallel, but in 4 of the 7 database, it is a really a huge and long process.
Combining the INSERTs loads from the 7 databases in only one will be impossible.......,,,,
But, IzPCA recently introduced a new feature, called "Continuous Collector".
By using that feature, small amounts of data will be transmited to the central LPAR at every 5 minutes (or even less), processed immediately,in a short period of time, and withsmall use of CPU, instead of one or two transmissions by day, of very large amounts of data and the corresponding collect jobs occurring only once or twice a day, with long elapsed times, and huge comsumption of CPU
I suspect the total CPU seconds consumed will be more or less the same in both cases, but in the new method it will occur insmall bursts many times a day!!
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.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
