Google BigQuery vs. IBM Cloud Bare Metal Servers

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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Well suited - 1) To Install required Products/Software in a middleware technology 2) Customize the file system and size of the storage 3) Install required monitoring tools like Tivoli, Splunk, etc. Less appropriate - 1) Maybe for SAAS products that do not require all of the installations.

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• 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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• Performance - the servers perform really well, even under stress. We have some long build processes running concurrently, and the server [can] serve other applications without any problems.
• Secure - for the most part, the servers are very secure and IBM provides many tools to help [make] sure the servers stay that way.
• Highly Available - while we have experienced various downtimes and outages with other IBM Cloud offerings, so far, we have not experienced any with [IBM Cloud] Bare Metal Servers.

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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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• [In my experience, the] Customer Service Agreement (CSA) has many gaps in terms of responsibility with Bare Metal Servers.
• [I believe] IBM should be deploying servers and firmware updating all components before providing them to customers to prevent component failure.
• [I feel] IBM needs lots of improvement with their legacy VPN to access IMPI management tools. The level of security of it is unparalleled when it works. Having access to KVM / IPMI is critical for any business, and when their VPN service is not working.
• [From my experiences,] IBM deployed faulty hardware, or failed to update firmware per Lenovo notices, only to pass off blame.
• [In my opinion,] IBM's General Counsel and Paralegal held our data/company hostage when components failed, [in my experience] to IBM "gross negligence" (in their words), only to release it if we were to limit damages to $1,000.

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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Due to cloud computing taking over the market, I have moved to cloud computing. It is so much easier upgrading or downsizing a virtual server on the cloud vs bare metal. I find it way more convenient on cloud computing. The provisioning takes way too long for bare metal servers.

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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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Great responsiveness and detailed know-how from the team. Self Explanatory and good resources on the Web to resolve issues. Good communication on issues via email. Good response times on issues which arise and where we have received support from the IBM support team. We believe that IBM is a great Partner to base our IT applications and we believe that a critical infrastructure like a cloud backend will be well served if we continue to base it on IBM.

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The implementation of this software took place as we planned. The performance time taken for full functionality was very reliable with positive results. The customer support team was the best team I have ever met in my career experience. They are always with timely responses when reached to offer any help.

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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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The Best part of this IBM Cloud Bare Metal Servers is performance and a very highly usable part is Security stuff. everyone needs to secure their data and work with a smoothly running app. for this reason I select this server rather than another one. I will use it in feature [definitely].

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Invited by TrustRadius (TR) on Vendor's Behalf

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

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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• Future readiness--we have the ability to quickly respond and build custom demos for customers in a short timeframe.
• Savings on hardware--we have the ability to spin up large workloads to demonstrate to customers the performance of our tools and IBM Cloud.

Incentivized

Invited by TrustRadius (TR) on Vendor's Behalf

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