Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Oracle Autonomous Database
Score 8.9 out of 10
N/A
Oracle Autonomous Database provides a self-driving, self-securing, self-repairing cloud service that eliminate the overhead and human errors associated with traditional database administration. Oracle Autonomous Database takes care of configuration, tuning, backup, patching, encryption, scaling, and more.
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.
Pro - Stability. Does everything anyone could need. If it's not there it will be on the next update. There is plenty of support for it. It's been around for a long time and it's reliable. The support is well documented and has a great reputation. Cons - Errors have been found in the documentation provided by Oracle with guidelines, etc. Oracles salespeople have a reputation of being obnoxious and condescending.
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.
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.
There is no access to the physical host of the DB. This is expected from a managed DB. Everything must be done through the console or via API calls. This is a new learning curve for the DBAs.
Due to the lack of physical host access, certain features are not supported, such as Transportable tablespaces and Oracle LogMiner.
Certain special data types, (such as XMLType) are not allowed; be sure the app vendor certifies their product on this platform.
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.
Because it does exactly what we need: it enables us to manage our development and testing database environments in a quick and simple way without requiring support from a database administrators team.
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
The product is continuously evolving and new features are added frequently. Management options through the OCI (Oracle Cloud Infrastructure) console and through the command line and API are being enhanced frequently.
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.
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.
I found Oracle Autonomous Database very secure to store data and private information.I always feel secure with Oracle Autonomous Databases disaster recovery features.It is very effective to build applications for mobile and desktop devices lesser code using a low code development framework namely Oracle Application Express (ApEx).
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.
