Google BigQuery vs. RavenDB

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

If you're a.NET developer searching for a system other than SQL Server for business assessment, then you must try RavenDB. RavenDB is a fantastic document-oriented system that has been specifically developed to work with all.NET or Windows systems. Developers are continually working on such systems to eliminate their flaws while also providing a few benefits. We must refresh ourselves on a regular basis since the free software system is like an open area where anybody may stand up with a brilliant solution to the issue. RavenDB is absolutely worth a look

• 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

• Document Database - no Object-Relational Impedance Mismatch
• ACID support that is optimized for performance
• Can be easily integrated into automated tests (unit tests)
• Easily configurable via C# code
• Comes directly with RavenStudio - no SSMS or SQL Developer required
• In general low footprint when it comes to memory and disk consumption
• Useful safety nets for new developers - e.g. by default an exception is thrown when you make too many requests within a session

• 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

• The documentation is very good, but it's sometimes hard to find the topic I'm looking for.
• Updating references is done manually. It would be nice if there was a feature to help with that. I'm not sure that's even possible though.

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

We've had an excellent experience using RavenDB. Internally we are testing the newer features in 5.0 such as time series, which will effect the con specified previously dependent on the real world performance. We foresee that BattleCrate will continue to use RavenDB as we grow.

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

Really good .NET client that is very easy to use. The management studio is excellent and puts anything that Microsoft or Oracle have to shame. Very quick to develop with once the complexity hurdle has been overcome. Initially using it can be a bit painful until you fully grasp the event sourced nature of the indexing.

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

The support is really fast and flexible. Since one single working day, we got a response to our first request, only 4 days later we got a technical demonstration for our complete developer team to get in touch with raven and its performance. Also during our development, we got a quick response to questions.

RavenFS changed along the way and made us change the codes.

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

The given alternatives are also powerful and really good noSQL databases but the highest availability of RavenDB allows me/us to know it a lot better. RavenDB is encrypted by default wherever we use it in production and it has a high level of documents compression.

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

• RavenDB has saved my customers a lot of money with their cloud services' tiered model. The database is able to grow with the project/company and can start out small at a low cost.
• RavenDB is free for three nodes and three CPUs, which makes it great for development scenarios. You're able to start rapidly building applications without having to worry about licensing.
• Scaling out has allowed us to use three small cloud servers when starting out and get the performance and throughput of a single larger server.