Google BigQuery vs. Google Universal Analytics (discontinued)

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).

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As I have discussed previously their insights were very useful. The second thing is since it is a Google product you will connect the data very easily from other platforms like Bigquery, Google Drive, etc. and even you can connect Google marketing platform. through this tool, you can track your live campaign how they were performing, and how it will be engaging your customer as well.

• Realtime integration with Google Sheets.
• 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.

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• It is an excellent cloud analytics platform that is easy to install and configure and easy to deploy and use, allowing us to measure web traffic and other tools.
• It is an entirely online tool; it does not take up hard disk space like other desktop tools.
• Since this tool is draggable, Google is constantly adding more features.
• Even beginners who do not have a custom dashboard can get information. If there is a problem somewhere on the site that needs to be investigated, Google Analytics 360 will notify you.

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• 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.

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• Generally I think there is a lot you can do within the tool, but as it is a Google product it means there is limited support - something which I think lets all of the platform stacks down
• There could be more visual signifiers to identify if a feature is a normal or 360 feature. This would mean you can really get to grips with what the extra more advanced elements are

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.

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Google Analytics 360 is an upgraded version of the most widely used web/app analytics tracking tools in the market. The price is stable and predictable making it a long-term product of choice. It's easy to use and pairs so well with other Google Marketing Platform products.

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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.

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The UI is very easy to navigate and use. The features are well designed and intuitive. As long as the user has a good understanding of basic digital analytics definitions and capabilities, this tool should be quite easy to use. I consider Google Analytics Premium to be the easiest of all of the enterprise solutions out there to use.

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.

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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.

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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.

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If you purchase Premium through a reseller like LunaMetrics, you are going to be taken care of. The additional amount of support and services that a reseller provides to make sure you have the best experience with the product is the reason why the reseller program exists to begin with. Support doesn't have to be just reactive, it can be proactive as well.

There is a ton of information online about Google Analytics, but Google Analytics Premium users will have dedicated support and training from Google or an Authorized Reseller.

If you already have the basic version of GA installed, "getting" GA Premium happens immediately through a virtual flipping of the switch - no need to re-implement. You'll want to expand your use of custom dimensions and metrics (you get 10x the amount with Premium). Ideally, you'll be using a tag management solution to talk with GA Premium, in concert with implementing a dataLayer (to note, Google's Tag Manager platform is covered under the same GA Premium SLA, and it's free). There are some welcomed "configurations" with GA Premium, such as integrating with DoubleClick products, activating data driven attribution models, and building roll-up executive reports - but all of these are easy point and click solutions. In comparison with any other enterprise analytics solution, implementing GA and GA Premium is traditionally easier and more flexible. And if you have any trouble or need an extra set of hands for implementation, GA Certified Partners like LunaMetrics can help

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.

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Unless you have very complex and edge case analytics needs, Google Analytics [360 (formerly Google Analytics Premium)] is likely going to be the best choice. From both a cost and usability stand point, Google wins. Adobe has the edge case when you need to create really custom reports, dimensions, metrics, etc. In my experience, this is rarely the case and you end up biting off more than you can chew. Stick with Google unless you are or plan on hiring an Adobe Analytics expert.

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None so far. Very satisfied with the transparency on contract terms and pricing model.

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.

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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.

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• It helps me understand which social media platforms are most successful for me - which I should focus on and which I might want to focus less on.
• I can also see which blog posts people are reading - so I know which topics resonate most. I can write more of those, hopefully gaining more visitors.

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