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).
K8s should be avoided - If your application works well without being converted into microservices-based architecture & fits correctly in a VM, needs less scaling, have a fixed traffic pattern then it is better to keep away from Kubernetes. Otherwise, the operational challenges & technical expertise will add a lot to the OPEX. Also, if you're the one who thinks that containers consume fewer resources as compared to VMs then this is not true. As soon as you convert your application to a microservice-based architecture, a lot of components will add up, shooting your resource consumption even higher than VMs so, please beware. Kubernetes is a good choice - When the application needs quick scaling, is already in microservice-based architecture, has no fixed traffic pattern, most of the employees already have desired skills.
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.
Local development, Kubernetes does tend to be a bit complicated and unnecessary in environments where all development is done locally.
The need for add-ons, Helm is almost required when running Kubernetes. This brings a whole new tool to manage and learn before a developer can really start to use Kubernetes effectively.
Finicy configmap schemes. Kubernetes configmaps often have environment breaking hangups. The fail safes surrounding configmaps are sadly lacking.
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.
The Kubernetes is going to be highly likely renewed as the technologies that will be placed on top of it are long term as of planning. There shouldn't be any last minute changes in the adoption and I do not anticipate sudden change of the core underlying technology. It is just that the slow process of technology adoption that makes it hard to switch to something else.
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.
It is an eminently usable platform. However, its popularity is overshadowed by its complexity. To properly leverage the capabilities and possibilities of Kubernetes as a platform, you need to have excellent understanding of your use case, even better understanding of whether you even need Kubernetes, and if yes - be ready to invest in good engineering support for the platform itself
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.
Most of the required features for any orchestration tool or framework, which is provided by Kubernetes. After understanding all modules and features of the K8S, it is the best fit for us as compared with others out there.
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.
