For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
Cost Effective & Flexible: Customers can start as low as a single OCPU VM up to 24 OCPUs. Customers pay only for OCPUs and Storage used.
Ease Of Getting Started: Customers can easily create Oracle Certified, full-featured, fully supported 11g, 12c (both 12.1 & 12.2) databases with choice of any database edition.
Built-in High Availability Constructs: Customers can easily deploy 2-node RAC configurations with all the VM shapes. For example: Easily deploy a 2-node RAC configuration with 2 core Virtual Machines and shared block storage of up to 40 TB.
Durable & Scalable Storage: Customers can use remote storage starting at 256GB up to 40 TB. Storage can be scale up with no downtime.
Secure: Customers still get all the advantages of our Oracle IAM for management control and VCN Security lists for securing their database environments.
Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
When we restart the DBaaS instance, it seems like we had to add the NIC network back again. I'm not sure if it's specific to our instance configuration!
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.
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
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.
Google's Firebase isn't a competitor but we had to use Google's BigQuery because Google's Firebase's database is limited compared to Google's BigQuery. Linking your Firebase project to BigQuery lets you access your raw, unsampled event data along with all of your parameters and user properties. Highly recommend connecting the two if you have a mobile app.
I would prefer the oracle database as service where my complete implementation is on Oracle Cloud Platform and as BI Implementation where datawarehouse is built on oracle database.
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.
Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.
Billing on Hosted Environment per hour, OCPU per hour, block volumes, object storage, etc.
Costing & maintenance, patching.
Security & TDE cycles.
Backups & recovery.
The features are complemented by database lifecycle management features, like configuration management, performance management, patch automation, etc. which make the solution complete from a DBaaS administrator’s perspective as well.
Manager 12c covers all the major use cases for DBaaS, which yield significant business benefits and high ROI.
