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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Oracle Exalogic in conjunction with an Oracle Exadata based platform is what I would consider the best of breed solution; however the performance may be overkill for what you need. Don't spend the money unless you need the performance, Oracle offers other solutions at a much lower cost. Purchase what you need not what's the shiny new product.
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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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Private Cloud: in the virtual configuration, you can create several accounts and assign different resources (vCPU, RAM, etc.) to several departments in your organization.
SDP & Infiband: Oracle Exalogic can be connected to Oracle Exadata using Infiniband Fabric, to take advantage of high bandwidth and low latency network to connect Weblogic to Oracle Database.
Standardization: Oracle Weblogic Server running on Oracle Exalogic is the same software running on a normal Linux machine, so you can easily move Java applications without changing it, and immediately benefit from the Exalogic Optimizations.
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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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Currently, the management of Exalogic is a little arcane. There is a good chance that Oracle can bring flexibility into the control stack because we have seen changes with each version of the software Echo was an improvement and foxtrot even more so. The ability to easily change VM shapes was another welcome change.
Again the fact arises that to build a very high performance machine there will be idiosyncrasies and a certain amount of retraining may be required. I think this is one area where Exalogic lacks not as a product but as a solution is that there isn't as much good knowledge available about it as there is for other engineered systems.
The Exalogic default setup could do with an SSD storage option, currently the onboard comes with a spinning disk.
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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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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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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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It is a very condensed version of what used to be rows of servers. I like that storage, networking and compute nodes fit in one rack. The power and the software are top notch. The only problem is cost. You need to do some serious processing to get the true value out of the Exalogic system.
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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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It's hard to compare Oracle Exalogic Engineered Systems with anything else on the market. It's so purpose built for application performance and intended to be used in conjunction with other Oracle Engineered Systems. As I alluded to earlier in this review the cost may not be worth the investment if the performance isn't needed. Oracle offers other solutions like the Oracle PCA which will meet the need for licensing compliance at a much lower cost of ownership and may fit better into your current infrastructure.
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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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
