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)
Mapp Intelligence, powered by Webtrekk
Score 10.0 out of 10
N/A
Mapp Intelligence, powered by Webtrekk, is a Customer Data Platform that not only collects
and analyzes user data and behavior from apps and websites, but also
personalizes experiences based upon this data. According to the vendor, Mapp Intelligence features a powerful
filter engine with extremely refined user-segmentation capabilities. Mapp Intelligence marketing apps allow personalization via website layers, banners and product
recommendations for users based on past visit data, including…
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Pricing
Google BigQuery
Mapp Intelligence, powered by Webtrekk
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
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Google BigQuery
Mapp Intelligence, powered by Webtrekk
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
Yes
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No setup fee
No setup fee
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Pricing is based on traffic volume. There is no setup fee, no yearly license and free customer support.
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Google BigQuery
Mapp Intelligence, powered by Webtrekk
Features
Google BigQuery
Mapp Intelligence, powered by Webtrekk
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
80 Ratings
0% above category average
Mapp Intelligence, powered by Webtrekk
-
Ratings
Automatic software patching
8.017 Ratings
00 Ratings
Database scalability
9.179 Ratings
00 Ratings
Automated backups
8.524 Ratings
00 Ratings
Database security provisions
8.773 Ratings
00 Ratings
Monitoring and metrics
8.475 Ratings
00 Ratings
Automatic host deployment
8.013 Ratings
00 Ratings
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Google BigQuery
Mapp Intelligence, powered by Webtrekk
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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).
Webtrekk is a good tool to get insights on your website and app usage. It is possible to get more information on your campaigns and engagements. If Webtrekk is in use with Mapp Engage, it is possible to manage your campaigns with one tool. It is not possible to use Webtrekk as data lake, but Mapp is actually developing a Customer Data Platform. Webtrekk (Mapp Intelligence) is a web analytics tool, so it has to be used as web analytics tool.
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.
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.
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 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.
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.
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.