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).
The Paradigm geologic and petrophysical interpretation software suite offers a complete package for integrated projects where a team starts from offset well data and leads all the way to mapping of horizons, properties and reservoir models. Geolog, the petrophysical module, is very powerful and versatile. Although they offer separate add-ons, for smaller projects where the petrophysical interpretation is done by a third-party, even the minimum package might be overkill.
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.
Geolog is extremely robust and powerful in handling large volumes of logs per well and a large number of wells, which is an asset for basin-scale projects.
Geolog is very flexible in terms of customizing log layout presentations and calculating anything via mathematical or logical expressions.
Epos display of fluid production data is very useful in cross-analyzing maturity and organic content versus hydrocarbons produced.
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.
Geolog could use a LAS batch loading graphical interface that doesn't require the use of prompt commands.
Geolog's geologic intervals display has improved in the last versions, but it's still cumbersome to set up and maintain (i.e. adding new intervals, changing stratigraphy).
SKUA-Paradigm should support geographic coordinates in order to avoid the workaround of creating pseudo-coordinates across UTM zone boundaries.
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.
Regarding the petrophysical modules, I have used other petrophysical software packages, and Paradigm is superior in data handling, visualization, flexibility, and batch calculations. Regarding the mapping and modeling, Paradigm is on par with other software I used on the same scale. SKUA-Gocad has some advantages over its main competitor, in terms of data manipulation, but the inability to handle geographic coordinates has a negative impact on my work efficiency.
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.
Paradigm Geolog has allowed us to handle the vast quantities of well log data in order to achieve our research goals and deliver the results to the sponsoring companies.
Paradigm Epos and SKUA-GOCAD have made possible to map the results of our petrophysical analysis on a basin-wide scale encompassing two provinces, without which our research deliverables would not have been possible.
