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)
SAP Integration Suite
Score 8.5 out of 10
N/A
SAP Integration Suite is an integration platform-as-a-service (iPaaS) that helps quickly integrate on-premises and cloud-based processes, services, applications, events, and data. It is used to accelerate innovation, automate more processes, and realize a faster time to value.
$11,199
per year
Pricing
Google BigQuery
SAP Integration Suite
Editions & Modules
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
Google BigQuery
SAP Integration Suite
Free Trial
Yes
Yes
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
Access to free tier services does not expire while there is an active Pay-As-You-Go or CPEA account with SAP. Once a free tier service limit has been reached users have the option to update from a free to a paid service plan in the same account.
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).
In our case to have a such a poweful middleware in the cloud, give us a lot of benefits such as maintenance and support. In the integration part to be able to connect SAP and Non SAP applications makes SAP Integration Suite a good investment when our master data in this case is in S4HANA. Less appropriate is that sometimes the updates in production tenant failed and they have to downgrade or repair the issues. Affecting the usage of the tool. I guess SAP team have to be more aware of performing the changes and tested well on development environments and then when they know for sure that is the correct way to go with the update put it in production.
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.
Provide more pre-built integrations to use within SuccessFactors or other modules instead of everything having to be custom built
Support is unable to provide advice on custom builds so you often have to engage a 3rd party partner
Works best when you have the functional and technical teams working together. Otherwise, the system is too technical for a functional user to create integration and a technical user not always understand the functional perspective
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.
It is in place, our system integrators are familiar with it, and it fits into the ecosystem. A better user interface, flow build and debugging experience would see it grow, many technical staff do not enjoy using it for this reason, however it is quite capable and powerful behind this one shortcoming.
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.
The user interface is messy and not intuitive. It has a steep learning curve, and flows developed around are easy to make a mess with layout and can be difficult to follow. The debugging is also quite difficult, it takes some time to figure out how to follow the flow and examine data. Error handling is also difficult and not intuitive, it is better to let some errors leak and monitor through ALM.
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.
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.
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.
The support for SAP Integration Suite is satisfactory. We leverage SAP support through our manage services partner. So far, we have not had many major issues. One concern, to make our rating a ten, would be turnaround time on high priority incidents. SAP Integration Suite drives our key business functions forward. Without a reasonable service level agreement on turnaround, we sometimes find us running into issues running pay, etc.
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.
SAP Integration Suite was already part of our SAP stack, part of Business Technology Platform, with out-of-the-box integration with S/4 HANA transactional and ERP system that we are using as our main back-end. Thus, we are achieving significant Total Cost Optimization benefits or running both solutions on the same platform, hosted on Azure cloud.
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.
