248 Reviews and Ratings
39 Reviews and Ratings
Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.Incentivized
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.Incentivized
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.Incentivized
Eliminates the requirement of hardware and installation.Minimizes the cost of operation and maintenance.Scalable to meet the increase in requirements.Enhances integrity, connectivity and performance of our applications.Reliable in terms of data security.Improved speed of querying and searching.Incentivized
It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.Incentivized
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!Incentivized
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.Incentivized
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.Incentivized
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.Incentivized
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.Incentivized
None so far. Very satisfied with the transparency on contract terms and pricing model.
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.
Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free.Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized.The visuals of ML models is very helpful to fine tune training, model building and prediction, etc.Incentivized
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.Incentivized